feat: slim meal_diary — derive name and nutrition from dish/recipe

Remove denormalized columns (name, calories, protein_g, fat_g, carbs_g)
from meal_diary. Name is now resolved via JOIN with dishes/dish_translations;
macros are computed as recipe.*_per_serving * portions at query time.

- Add dish.Repository.FindOrCreateRecipe: finds or creates a minimal recipe
  stub seeded with AI-estimated macros
- recognition/handler: resolve recipe_id synchronously per candidate;
  simplify enrichDishInBackground to translations-only
- diary/handler: accept dish_id OR name; always resolve recipe_id via
  FindOrCreateRecipe before INSERT
- diary/entity: DishID is now non-nullable string; CreateRequest drops macros
- diary/repository: ListByDate and Create use JOIN to return computed macros
- ai/types: add RecipeID field to DishCandidate
- Update tests and wire_gen accordingly

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

client/lib/features/scan/recognition_service.dart

@@ -65,6 +65,7 @@ class ReceiptResult {
 
 /// A single dish recognition candidate with estimated nutrition for the portion in the photo.
 class DishCandidate {
+  final String? dishId;
   final String dishName;
   final int weightGrams;
   final double calories;
@@ -74,6 +75,7 @@ class DishCandidate {
   final double confidence;
 
   const DishCandidate({
+    this.dishId,
     required this.dishName,
     required this.weightGrams,
     required this.calories,
@@ -85,6 +87,7 @@ class DishCandidate {
 
   factory DishCandidate.fromJson(Map<String, dynamic> json) {
     return DishCandidate(
+      dishId: json['dish_id'] as String?,
       dishName: json['dish_name'] as String? ?? '',
       weightGrams: json['weight_grams'] as int? ?? 0,
       calories: (json['calories'] as num?)?.toDouble() ?? 0,