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/serving/venue_pricing_benchmarks.sql

@@ -0,0 +1,57 @@
+-- Per-city pricing and occupancy benchmarks from Playtomic availability data.
+-- Aggregates venue-level daily metrics (last 30 days) into city-level benchmarks.
+-- Consumed by: planner defaults (pre-fill), city market profile, SEO articles.
+--
+-- Minimum data threshold: venues with >= 3 days of observations.
+
+MODEL (
+  name serving.venue_pricing_benchmarks,
+  kind FULL,
+  cron '@daily',
+  grain (country_code, city)
+);
+
+WITH venue_stats AS (
+  -- Aggregate last 30 days per venue
+  SELECT
+    da.tenant_id,
+    da.country_code,
+    da.city,
+    da.price_currency,
+    AVG(da.occupancy_rate)                              AS avg_occupancy_rate,
+    MEDIAN(da.median_price)                             AS median_hourly_rate,
+    MEDIAN(da.median_price_peak)                        AS median_peak_rate,
+    MEDIAN(da.median_price_offpeak)                     AS median_offpeak_rate,
+    AVG(da.estimated_revenue_eur)                       AS avg_daily_revenue,
+    MAX(da.active_court_count)                          AS court_count,
+    COUNT(DISTINCT da.snapshot_date)                    AS days_observed
+  FROM foundation.fct_daily_availability da
+  WHERE da.snapshot_date >= CURRENT_DATE - INTERVAL '30 days'
+    AND da.occupancy_rate IS NOT NULL
+    AND da.occupancy_rate BETWEEN 0 AND 1.5
+  GROUP BY da.tenant_id, da.country_code, da.city, da.price_currency
+  HAVING COUNT(DISTINCT da.snapshot_date) >= 3
+)
+SELECT
+  country_code,
+  city,
+  price_currency,
+  COUNT(*)                                              AS venue_count,
+  -- Pricing benchmarks
+  ROUND(MEDIAN(median_hourly_rate), 2)                  AS median_hourly_rate,
+  ROUND(MEDIAN(median_peak_rate), 2)                    AS median_peak_rate,
+  ROUND(MEDIAN(median_offpeak_rate), 2)                 AS median_offpeak_rate,
+  ROUND(PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY median_hourly_rate), 2) AS hourly_rate_p25,
+  ROUND(PERCENTILE_CONT(0.75) WITHIN GROUP (ORDER BY median_hourly_rate), 2) AS hourly_rate_p75,
+  -- Occupancy benchmarks
+  ROUND(MEDIAN(avg_occupancy_rate), 4)                  AS median_occupancy_rate,
+  ROUND(AVG(avg_occupancy_rate), 4)                     AS avg_occupancy_rate,
+  -- Revenue benchmarks (per venue per day)
+  ROUND(MEDIAN(avg_daily_revenue), 2)                   AS median_daily_revenue_per_venue,
+  -- Court mix
+  ROUND(MEDIAN(court_count), 0)::INTEGER                AS median_court_count,
+  -- Data quality
+  SUM(days_observed)                                    AS total_venue_days_observed,
+  CURRENT_DATE                                          AS refreshed_date
+FROM venue_stats
+GROUP BY country_code, city, price_currency