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.

frontend/src/pages/VehicleDetail.tsx

@@ -112,6 +112,7 @@ export function VehicleDetail() {
           {vehicle.quality_issues.length === 0 && <li>No quality issues recorded.</li>}
           {vehicle.quality_issues.map((q) => (
             <li key={q.public_ref}>
+              <Link to={`/data-quality/${q.public_ref}`}>{q.public_ref}</Link>
               <SeverityBadge severity={q.severity} />
               <span>{q.rule_type.replace(/_/g, " ")}</span>
               <StatusBadge status={q.status} />