Support Filter Pushdown for Spark Structured Streaming Reads

### Feature Request / Improvement

Spark batch reads from Iceberg tables push down their filter expressions, enabling manifest-level pruning (via partition range summaries), file-level pruning (via column min/max statistics), and partition elimination. Spark structured streaming reads do not currently benefit from any of this, but it would be valuable to support filter pushdown in the `MicroBatchStream` path as well.

Today, a streaming query like `.readStream.format("iceberg").load("t").filter("partition_col = 'foo'")` will create Spark tasks for and read files across all partitions. The filter is only applied as a post-read record filter by Spark. For streaming reads with partition filters, this can create significant unnecessary I/O, task overhead, and compute cost.

The core API already supports this. `IncrementalAppendScan` inherits `filter(Expression)` from the `Scan` interface, and `BaseIncrementalAppendScan` correctly threads it to `ManifestGroup.filterData()` for the full pruning pipeline. The gap is in the Spark connector: `SparkScan.toMicroBatchStream()` does not pass filter expressions to `SparkMicroBatchStream`, so they are never applied.

Closing this gap would bring streaming reads to parity with batch reads for filter pushdown, benefiting both partition-based and column statistics-based pruning.

Affects all maintained Spark connector versions: v3.4, v3.5, v4.0, v4.1.

### Query engine

Spark

### Willingness to contribute

- [x] I can contribute this improvement/feature independently
- [x] I would be willing to contribute this improvement/feature with guidance from the Iceberg community
- [ ] I cannot contribute this improvement/feature at this time

Start at SparkScan.toMicroBatchStream() and trace how SparkMicroBatchStream receives scan filters; compare this with the batch path and the existing IncrementalAppendScan/BaseIncrementalAppendScan flow. Add focused streaming coverage for partition and column-statistics pruning, then run the relevant Spark connector tests; done means filters reach ManifestGroup.filterData() and unnecessary files are excluded.

Written by the indexing model from the issue text.