Azure ML filters mlflow.source.* tags at run creation but not at runtime

- **Package Name**: azureml-mlflow
- **Package Version**: 1.61.0.post1
- **Operating System**: Ubuntu 22.04.5 LTS
- **Python Version**: 3.12

**Describe the bug**
Azure ML's MLflow tracking backend filters `mlflow.source.name` and `mlflow.source.type` tags when passed to `create_run(tags=...)` but allows the same tags when set via `set_tag()` after run creation. This inconsistency breaks integrations with tools like PyTorch Lightning.

**To Reproduce**
Steps to reproduce the behavior:
1. Create a run with source tags at creation time:
```python
client.create_run(experiment_id="...", tags={
"mlflow.source.name": "https://github.com/org/repo",
"mlflow.source.type": "PROJECT"
})

Result: Tags are filtered out ❌

2. Create a run and set tags afterwards
run = client.create_run(experiment_id="...")
client.set_tag(run.info.run_id, "mlflow.source.name", "https://github.com/org/repo")
client.set_tag(run.info.run_id, "mlflow.source.type", "PROJECT")

Result: Tags are preserved ✅

**Impact**
PyTorch Lightning MLFlowLogger broken: Lightning passes tags to create_run(), causing all source tags to be lost
Standard MLflow patterns fail: The recommended MLflow pattern uses tags at creation
Confusing behavior: No documentation explains this filtering

**Expected behavior**
Either:
Option A: Allow mlflow.source.* tags in both scenarios (preferred)
Option B: Filter them in both scenarios and document why
Option C: Document the current behavior and provide guidance

**Additional context**
Noticed this difference when planning to migrate to Mlflow in AzureML from a self-hosted version of MlFlow v2.12.1.

Start at the azureml-mlflow tracking backend paths for create_run(tags=...) and set_tag(), reproducing both cases from the issue. Determine which expected behavior is accepted, then verify that source tags are handled consistently and the PyTorch Lightning integration scenario no longer loses them.

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