refactor(transform): conform geographic dimension hierarchy via city_slug

Propagates the conformed city key (city_slug) from dim_venues through the
full pricing pipeline, eliminating 3 fragile LOWER(TRIM(...)) fuzzy string
joins with deterministic key joins.

Changes (cascading, task-by-task):
- dim_venues: add city_slug computed column (REGEXP_REPLACE slug derivation)
- dim_venue_capacity: join foundation.dim_venues instead of stg_playtomic_venues;
  carry city_slug alongside country_code/city
- fct_daily_availability: carry city_slug from dim_venue_capacity
- venue_pricing_benchmarks: carry city_slug from fct_daily_availability;
  add to venue_stats GROUP BY and final SELECT/GROUP BY
- city_market_profile: join vpb on city_slug = city_slug (was LOWER(TRIM))
- planner_defaults: add city_slug to city_benchmarks CTE; join on city_slug
- pseo_city_pricing: join city_market_profile on city_slug (was LOWER(TRIM))
- pipeline_routes._DAG: dim_venue_capacity now depends on dim_venues, not stg_playtomic_venues

Result: dim_venues.city_slug → dim_cities.(country_code, city_slug) forms a
fully conformed geographic hierarchy with no fuzzy string comparisons.

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

transform/sqlmesh_padelnomics/models/foundation/dim_venue_capacity.sql

@@ -34,6 +34,7 @@ SELECT
   v.tenant_id,
   v.country_code,
   v.city,
+  v.city_slug,
   cc.active_court_count,
   ROUND(wh.hours_open_per_week, 1)                AS hours_open_per_week,
   ROUND(wh.avg_hours_open_per_day, 1)             AS avg_hours_open_per_day,
@@ -42,6 +43,6 @@ SELECT
   ROUND(cc.active_court_count * wh.avg_hours_open_per_day, 1)  AS capacity_court_hours_per_day,
   -- Total bookable court-hours per week
   ROUND(cc.active_court_count * wh.hours_open_per_week, 1)     AS capacity_court_hours_per_week
-FROM staging.stg_playtomic_venues v
+FROM foundation.dim_venues v
 JOIN court_counts cc ON v.tenant_id = cc.tenant_id
 JOIN weekly_hours wh ON v.tenant_id = wh.tenant_id