feat: require date_column in training parameters and update documentation

- Made `date_column` a required field in `TrainModelParams`, ensuring it must be present in the input data.
- Updated related documentation in `input-sample.md`, `README.md`, and various test scenarios to reflect the change in requirement.
- Adjusted the handling of `date_format` to default to `yyyy-MM-dd HH:mm:ss` if omitted, enhancing usability.
- Refined test scenarios to include new examples and ensure compliance with the updated parameter structure.

These changes improve the robustness of the model training workflow and clarify the expectations for input data.

model_manager/workflows/cleanup_files.py

@@ -32,7 +32,7 @@ class CleanupFiles:
     """
 
     @workflow.run
-    async def run(self, input_data: dict[str, Any]) -> None:
+    async def run(self, input_data: dict[str, Any] | None = None) -> None:
         """
         Execute the cleanup workflow.
 
@@ -40,7 +40,8 @@ class CleanupFiles:
         in sequence. No exception handling is needed as activities handle their
         own errors and notifications.
         """
-        temp_path = REPORTS_TEMP_DIR
+        payload = input_data or {}
+        temp_path = payload.get('temp_path') or REPORTS_TEMP_DIR
 
         # Metadata for tracking
         metadata = {