[fix] Improve datetime handling in YahooNormalize

[yang-i-hu]

Description

This PR fixes critical datetime parsing issues and deprecated pandas method warnings in the Yahoo Finance data collector. The changes address:

1. Fixed datetime parsing errors - Handles mixed datetime formats including timezone-aware timestamps that caused ValueError and AttributeError exceptions
2. Resolved deprecated pandas methods - Replaced deprecated fillna(method="ffill") with modern ffill() method
3. Improved timezone handling - Added robust timezone conversion logic to handle both timezone-aware and timezone-naive datetime indexes
4. Enhanced error handling - Implemented fallback strategies for datetime parsing with proper exception handling

Key Changes:

• Datetime parsing improvements: Added try-catch logic with multiple fallback strategies for handling different datetime formats (standard YYYY-MM-DD, mixed timezone formats, ISO formats)
• Timezone handling: Proper detection and conversion of timezone-aware indexes using tz_convert(None) vs tz_localize(None)
• Pandas compatibility: Updated deprecated fillna(method="ffill") to ffill() method
• Error prevention: Added utc=True parameter to eliminate FutureWarnings about mixed timezone parsing

Motivation and Context

Problem: The Yahoo data collector was failing during normalization with multiple critical errors:

1. ValueError: unconverted data remains when parsing with format "%Y-%m-%d": " 09:30:00-04:00" - caused by mixed datetime formats in the data
2. AttributeError: 'Index' object has no attribute 'tz_localize' - caused by improper timezone handling after mixed format parsing
3. FutureWarning: Deprecated pandas fillna(method="ffill") method usage

Root Cause: The original code assumed all datetime data would be in simple YYYY-MM-DD format, but Yahoo Finance data sometimes contains timezone information and mixed formats that broke the parsing logic.

Solution: Implemented robust datetime parsing with multiple fallback strategies and proper timezone handling to ensure data processing continues smoothly regardless of input datetime format.

How Has This Been Tested?

• [x ] Pass the test by running: pytest qlib/tests/test_all_pipeline.py under upper directory of qlib.
• If you are adding a new feature, test on your own test scripts.

Custom Testing Performed:

1. Created comprehensive test scripts to verify the fix handles various datetime formats:

   • Standard YYYY-MM-DD format
   • Timezone-aware formats (e.g., "2020-01-01 09:30:00-04:00")
   • ISO datetime formats
   • Mixed format combinations

2. Tested with actual data from the Yahoo source directory containing 10,997 CSV files

3. Verified fix handles edge cases:

   • Mixed timezone data that previously caused crashes
   • Proper timezone conversion without AttributeError
   • Elimination of FutureWarnings

Screenshots of Test Results (if appropriate):

Types of changes

• Fix bugs
• Add new feature
• Update documentation

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