Allow providing Arrow schema when scanning Parquet files

### Is your feature request related to a problem or challenge?

When scanning Parquet files, we'd often like to provide an expected schema, since:

1. The Parquet files might not all have an identical physical schema, but we may know the unified schema up front, such as when we are using a table format like Delta Lake.
2. A given Parquet type can map to many different Arrow types. For example, when possible it would be nice to read a string column as a DictionaryArray, especially if the data is already dictionary-encoded.

### Describe the solution you'd like

It would be nice to be able to provide an Arrow schema in `ParquetScanOptions`, and then the scan would try in order:

1. Read the Parquet data directly into the output type
2. Read the Parquet data into a supported Arrow type then cast
3. Return an error stating which types can't be mapped

This probably would require some changes upstream in the parquet crate, but for at least some of the functionality datafusion seems like the right place. LMK if you think differently.

### Describe alternatives you've considered

Right now our current issue is that our expected schema (from the Delta Lake log) doesn't match the physical schema (at least when written by Spark). So as a workaround we are looking at the metadata of one of the Parquet files. This will partially solve the issue, but will likely fail for tables where Spark wasn't the only engine to write to the table.

### Additional context

Here's a simple example where PyArrow's scanner works but Datafusions doesn't seem to:

```python
import pyarrow as pa
import pyarrow.parquet as pq

tab1 = pa.table({"a": pa.array([1, 2, 3, 4, 5], type=pa.int32())})
tab2 = pa.table({"a": pa.array([6, 7, 8, 9, 10], type=pa.int64())})

file1 = 'table/1.parquet'
file2 = 'table/2.parquet'

pq.write_table(tab1, file1)
pq.write_table(tab2, file2)
```

```python
import pyarrow.dataset as ds

ds.dataset("table", format="parquet").to_table()
```

```
pyarrow.Table
a: int32
----
a: [[1,2,3,4,5],[6,7,8,9,10]]
```

```python
from datafusion import SessionContext

# Create a DataFusion context
ctx = SessionContext()

# Register table with context
ctx.register_parquet('numbers', 'table')
```

```
Exception: DataFusion error: ArrowError(SchemaError("Fail to merge schema field 'a' because the from data_type = Int64 does not equal Int32"))
```

Start with ParquetScanOptions and the DataFusion integration with the parquet crate, then reproduce the provided PyArrow and DataFusion examples with differing physical schemas. Done should allow an expected Arrow schema to guide Parquet scanning, including supported casts, and report an error for unmappable types.

Written by the indexing model from the issue text.