M3: implement Data Quality Workbench

Five rule scanners (duplicate customers, missing fields, odometer regression, booking overlap, status conflict) run automatically after seed and via an explicit scan endpoint. Issue defer/reject/merge-customers endpoints with transactional customer merge (booking rewiring, tombstone, audit). Data Quality nav + workbench UI with two-column duplicate comparison and inline (non-native) confirm. Dashboard attention items now link to issues. 35 backend tests passing, ruff clean. Fixed a real false-positive bug in odometer-regression detection found through iteration on seed data, and two TS narrowing errors. Verified end-to-end via browser: S2 merge and S4 overlap scenarios.

backend/tests/test_seed.py

@@ -20,7 +20,8 @@ def test_seed_counts_match_deterministic_dataset():
         assert db.scalar(select(func.count()).select_from(Vehicle)) == 50
         assert db.scalar(select(func.count()).select_from(Customer)) == 180
         assert db.scalar(select(func.count()).select_from(Booking)) == 246
-        assert db.scalar(select(func.count()).select_from(DataQualityIssue)) == 15
+        # 15 from the CSV plus a deterministic set discovered by the post-seed scan.
+        assert db.scalar(select(func.count()).select_from(DataQualityIssue)) == 26
         assert db.scalar(select(func.count()).select_from(OutboxEvent)) == 20
         assert db.scalar(select(func.count()).select_from(User)) == 2
     finally: