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/serving/planner_defaults.sql

@@ -21,6 +21,7 @@ city_benchmarks AS (
   SELECT
     country_code,
     city,
+    city_slug,
     median_peak_rate,
     median_offpeak_rate,
     median_occupancy_rate,
@@ -128,7 +129,7 @@ SELECT
 FROM city_profiles cp
 LEFT JOIN city_benchmarks cb
   ON cp.country_code = cb.country_code
-  AND LOWER(TRIM(cp.city_name)) = LOWER(TRIM(cb.city))
+  AND cp.city_slug = cb.city_slug
 LEFT JOIN country_benchmarks ctb
   ON cp.country_code = ctb.country_code
 LEFT JOIN hardcoded_fallbacks hf