Treat unhandled Monte Carlo anomalies as data quality errors

[mars-lan]

🤔 Why?

In addition to user-defined monitors, Monte Carlo also supports automatic anomaly detection. We'd like to surface these anomalies as data quality issues, only if they're triggered and not marked as handled (acknowledged/expected/fixed) by the user

🤓 What?

• Use getAlerts GraphQL query to retrieve all ANOMALIES alerts and map unhandled ones to failed data monitors.
• Generalize _convert_dataset_name into _extract_dataset_name for MCON to dataset name parsing.
• Add configs to opt into the new behavior and to control the lookback length.

🧪 Tested?

Verified before & after against a production instance.

☑️ Checks

• My PR contains actual code changes, and I have updated the version number in pyproject.toml.

[github-actions]

☂️ Python Coverage

current status: ✅

Overall Coverage

Lines	Covered	Coverage	Threshold	Status
13525	12111	90%	85%	🟢

New Files

No new covered files...

Modified Files

File	Coverage	Status
metaphor/monte_carlo/config.py	100%	🟢
metaphor/monte_carlo/extractor.py	90%	🟢
TOTAL	95%	🟢

updated for commit: 165f048 by action🐍

[codecov]

Codecov Report

Attention: Patch coverage is 90.56604% with 5 lines in your changes missing coverage. Please review.

Project coverage is 89.54%. Comparing base (9b2b596) to head (165f048).
Report is 1 commits behind head on main.

Files with missing lines	Patch %	Lines
metaphor/monte_carlo/extractor.py	90.19%	5 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #1051      +/-   ##
==========================================
+ Coverage   89.53%   89.54%   +0.01%     
==========================================
  Files         211      211              
  Lines       13481    13525      +44     
==========================================
+ Hits        12070    12111      +41     
- Misses       1411     1414       +3     

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[alyiwang]

Otherwise, LGTM