[Python][Dataset] Add filters parameter to orc.read_table() for predicate pushdown

### Summary

Part 4 of ORC predicate pushdown (#48986). Depends on #49361.

Add a `filters` parameter to `pyarrow.orc.read_table()` for API parity with Parquet's `read_table()`. This makes ORC predicate pushdown accessible to Python users without requiring the lower-level Dataset API.

### Changes

**`python/pyarrow/orc.py`:**

Add `filters` parameter to `read_table()`. When specified, delegate to the Dataset API:

```python
def read_table(source, columns=None, filesystem=None, filters=None):
if filters is not None:
import pyarrow.dataset as ds
filter_expr = filters
if not isinstance(filters, ds.Expression):
filter_expr = ds.filters_to_expression(filters)
dataset = ds.dataset(source, format='orc', filesystem=filesystem)
return dataset.to_table(columns=columns, filter=filter_expr)
# ... existing non-filter path unchanged
```

**Supported filter formats:**

- Expression format: `ds.field('id') > 100`
- DNF tuple format: `[('id', '>', 100)]` (Parquet-compatible)
- Supported operators: `==`, `!=`, `<`, `>`, `<=`, `>=`, `in`, `not in`

**No Cython changes.** This is pure Python, reusing existing Dataset API bindings and the `filters_to_expression()` utility already used by Parquet.

### Examples

```python
import pyarrow.orc as orc
import pyarrow.dataset as ds

# Expression format
table = orc.read_table('data.orc', filters=ds.field('id') > 1000)

# DNF tuple format
table = orc.read_table('data.orc', filters=[('id', '>', 1000)])

# Multiple conditions (AND)
table = orc.read_table('data.orc', filters=[('id', '>', 100), ('id', '<', 200)])

# With column projection
table = orc.read_table('data.orc', columns=['id', 'value'],
filters=[('id', '>', 1000)])
```

### Tests

Tests in `python/pyarrow/tests/test_orc.py`:
- Expression format smoke test
- DNF tuple format smoke test
- Integration with column projection
- Correctness validation: filtered result matches post-filter of full read
- `filters=None` preserves existing behavior

### Component(s)

Python