feat: Playtomic pricing/occupancy pipeline + email i18n + audience restructure

Three workstreams:

1. Playtomic full data extraction & transform pipeline:
   - Expand venue bounding boxes from 4 to 23 regions (global coverage)
   - New staging models for court resources, opening hours, and slot-level
     availability with real prices from the Playtomic API
   - Foundation fact tables for venue capacity and daily occupancy/revenue
   - City-level pricing benchmarks replacing hardcoded country estimates
   - Planner defaults now use 3-tier cascade: city data → country → fallback

2. Transactional email i18n:
   - _t() helper in worker.py with ~70 translation keys (EN + DE)
   - All 8 email handlers translated, lang passed in task payloads

3. Resend audiences restructured to 3 named audiences (free plan limit)

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

transform/sqlmesh_padelnomics/models/foundation/dim_venues.sql

@@ -2,6 +2,8 @@
 -- Venues from both sources are unioned; near-duplicates (within ~100m) are
 -- collapsed to a single record preferring Playtomic data (richer metadata).
 -- Proximity dedup uses haversine approximation: 1 degree lat ≈ 111 km.
+--
+-- Playtomic venues include court counts, indoor/outdoor split, currency, and timezone.
 
 MODEL (
   name foundation.dim_venues,
@@ -10,51 +12,74 @@ MODEL (
   grain venue_id
 );
 
-WITH all_venues AS (
+WITH playtomic_venues AS (
   SELECT
-    'osm:' || osm_id::TEXT AS venue_id,
-    source,
+    'pt:' || v.tenant_id                            AS venue_id,
+    v.tenant_id,
+    'playtomic'                                     AS source,
+    v.lat,
+    v.lon,
+    v.country_code,
+    v.name,
+    v.city,
+    v.postcode,
+    v.tenant_type,
+    v.timezone,
+    v.vat_rate,
+    v.default_currency,
+    -- Court counts from resources
+    COUNT(r.resource_id)                             AS court_count,
+    COUNT(r.resource_id) FILTER (WHERE r.resource_type = 'indoor')   AS indoor_court_count,
+    COUNT(r.resource_id) FILTER (WHERE r.resource_type = 'outdoor')  AS outdoor_court_count,
+    v.extracted_date
+  FROM staging.stg_playtomic_venues v
+  LEFT JOIN staging.stg_playtomic_resources r
+    ON v.tenant_id = r.tenant_id AND r.is_active = TRUE
+  WHERE v.country_code IS NOT NULL
+  GROUP BY
+    v.tenant_id, v.lat, v.lon, v.country_code, v.name, v.city,
+    v.postcode, v.tenant_type, v.timezone, v.vat_rate,
+    v.default_currency, v.extracted_date
+),
+osm_venues AS (
+  SELECT
+    'osm:' || osm_id::TEXT                          AS venue_id,
+    NULL                                            AS tenant_id,
+    'osm'                                           AS source,
     lat,
     lon,
     country_code,
     name,
     city,
     postcode,
-    NULL AS tenant_type,
+    NULL                                            AS tenant_type,
+    NULL                                            AS timezone,
+    NULL                                            AS vat_rate,
+    NULL                                            AS default_currency,
+    NULL                                            AS court_count,
+    NULL                                            AS indoor_court_count,
+    NULL                                            AS outdoor_court_count,
     extracted_date
   FROM staging.stg_padel_courts
   WHERE country_code IS NOT NULL
-
-  UNION ALL
-
-  SELECT
-    'pt:' || tenant_id      AS venue_id,
-    source,
-    lat,
-    lon,
-    country_code,
-    name,
-    city,
-    postcode,
-    tenant_type,
-    extracted_date
-  FROM staging.stg_playtomic_venues
-  WHERE country_code IS NOT NULL
 ),
--- Rank venues so Playtomic records win ties in proximity dedup
+all_venues AS (
+  SELECT * FROM playtomic_venues
+  UNION ALL
+  SELECT * FROM osm_venues
+),
 ranked AS (
   SELECT *,
     CASE source WHEN 'playtomic' THEN 1 ELSE 2 END AS source_rank
   FROM all_venues
 )
--- Note: full proximity dedup (haversine clustering) is expensive in SQL.
--- For now, deduplicate on exact (country_code, ROUND(lat,3), ROUND(lon,3))
--- — ≈111m grid cells. Refine with spatial index if volumes grow.
+-- Deduplicate on ~111m grid cells, preferring Playtomic
 SELECT
   MIN(venue_id) OVER (
     PARTITION BY country_code, ROUND(lat, 3)::TEXT, ROUND(lon, 3)::TEXT
     ORDER BY source_rank
-  )                                                         AS venue_id,
+  )                                                 AS venue_id,
+  tenant_id,
   country_code,
   lat,
   lon,
@@ -62,11 +87,17 @@ SELECT
     MAX(CASE WHEN source = 'playtomic' THEN name END)
       OVER (PARTITION BY country_code, ROUND(lat,3)::TEXT, ROUND(lon,3)::TEXT),
     name
-  )                                                         AS name,
-  COALESCE(city, '')                                        AS city,
+  )                                                 AS name,
+  COALESCE(city, '')                                AS city,
   postcode,
   source,
   tenant_type,
+  timezone,
+  vat_rate,
+  default_currency,
+  court_count,
+  indoor_court_count,
+  outdoor_court_count,
   extracted_date
 FROM ranked
 QUALIFY ROW_NUMBER() OVER (