Add Python bindings for accessing ExecutionMetrics

[ShreyeshArangath]

Which issue does this PR close?

Closes #1379

Rationale for this change

Today, DataFusion Python only exposes execution metrics through formatted console output via explain(analyze=True). This makes it difficult to programmatically inspect execution behavior.

There is currently no structured python API to access per-operator metrics such as output_rows, elapsed_compute, spill_count and other runtime metrics collected during execution.

This PR introduces APIs to surface the execution metrics, mirroring the Rust API in datafusion::physical_plan::metrics.

What changes are included in this PR?

• Added plan caching to PyDataFrame so the physical plan used during execution is retained and available for metrics access.
• Kept the metrics() method and added collect_metrics() helper to walk the execution plan tree and aggregate metrics from all operators.

Are there any user-facing changes?

Users can now programmatically access execution metrics

  df = ctx.sql("SELECT * FROM t WHERE x > 1")
  df.collect()
  plan = df.execution_plan()
  metrics = plan.collect_metrics() 
  for operator_name, metrics_set in metrics:
      print(f"{operator_name}: {metrics_set.output_rows} rows")