Ilongin/1910 preserve pydantic class identity

[ilongin]

Summary

Preserve the identity of plain Pydantic models when reading saved datasets in a new process.

Previously, only DataModel subclasses were registered automatically. A plain BaseModel was reconstructed from its stored schema during deserialization, even when the original class had already been imported. The reconstructed class had the same name and fields but different Python identity, causing isinstance() checks and nested Pydantic validation to fail.

Closes #1910.

Changes

• Reuse an already-imported Pydantic class when its module, name, and complete model shape match the stored schema.
• Compare nested model graphs recursively, including models inside optional, list, mapping, tuple, and union annotations.
• Validate existing ModelStore matches before reusing them.
• Fall back to a synthetic model when the original class is unavailable or its shape has changed.
• Warn when an imported class has the expected identity but a different stored shape.
• Resolve classes only from sys.modules; dataset metadata never triggers a module import.
• Improve errors for malformed nested custom-type schemas.
• Update the Pydantic model documentation to describe automatic resolution and synthetic fallback.

Tests

• Added a cross-process regression test covering direct, list, and optional nested Pydantic models.
• Verified that the restored objects use the imported classes and can be passed back into their parent model.
• Verified that validators on the restored real class run during dataset reads.
• Added unit coverage for matching and mismatched model graphs, recursive models, malformed nested schemas, safe module lookup, and ModelStore behavior.

Fixes #1910

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