SIENTIAPDE-1222

Refactor datetime index handling in MLFlow and MLFlowRepository

- Moved the detect_and_parse_datetime_index method from MLFlow to MLFlowRepository for better organization and reusability.
- Updated the method to include enhanced logging and error handling for invalid datetime formats.
- Adjusted the transform method in MLFlowRepository to utilize the new datetime index parsing logic.
- Added unit tests for both valid and invalid datetime index cases to ensure robustness.

tests/laborious/activities/test_mlflow.py

@@ -1,8 +1,9 @@
+from datetime import datetime
 from unittest.mock import ANY, MagicMock, patch
 
 import numpy as np
-from pandas import DataFrame
-from pytest import fixture, mark
+from pandas import DataFrame, Timestamp
+from pytest import fixture, mark, raises
 from laborious.activities.mlflow import MLFlow
 from sientia_do.notifications.models import NotificationLevel
 
@@ -58,7 +59,7 @@ metadata = {
 @mark.asyncio
 @patch("laborious.activities.mlflow.DataFrame")
 @patch("laborious.activities.mlflow.max")
-async def test_request_transform(mock_max, mock_dataframe, mlflow):
+async def test_request_transform_success(mock_max, mock_dataframe, mlflow):
     mock_max.return_value = '2024-01-02'
     # Mock input data
     input_data = {