[Python][Parquet] Extending the schema and writing it back does not update Spark schema metadata

### Describe the bug, including details regarding any error messages, version, and platform.

I have a use case where I need to process files that have been originally written by PySpark with pyarrow and write them back so that they can again be read by PySpark. During the processing, I need to extend the schema.

When extending the schema and writing it with `flavor=spark` , then reading with PySpark does not show the added columns. Using other Parquet tools like pandas, polars or random online viewers, I can see that the data is there.

My guess for what is happening after some debugging is that PySpark uses not the parquet schema itself if there is metadata called `org.apache.spark.sql.parquet.row.metadata` that pyarrow does not update. When I call `replace_schema_metadata()` before writing the table in PyArrow, then PySpark can see the new column. I would expect writing with pyarrow using the spark flavor would handle this.

Code to reproduce:

`pip install pyarrow pyspark`

```python
from pyspark.sql import SparkSession
import pyarrow as pa
import pyarrow.parquet as pq

spark = SparkSession.builder.getOrCreate()
df = spark.createDataFrame(
[
(1, "foo"),
(2, "bar"),
],
["id", "label"] # add your column names here
)
df.write.mode("overwrite").parquet("table.parquet")
table = pq.read_table("table.parquet")

result = []
for batch in table.to_batches():
d = batch.to_pydict()
d["value"] = d["label"] * 2

schema = table.schema.append(pa.field("value", pa.string()))
enriched_table = table.from_pylist(result, schema=schema)
pq.write_table(enriched_table, 'enriched_table.parquet', flavor="spark")

print("# Pyarrow reading enriched")
print()
enriched_table2 = pq.read_table('enriched_table.parquet')
print(enriched_table2.schema)
print(enriched_table2.schema.metadata)

print("# Pyspark reading enriched")
print()
df = spark.read.parquet('enriched_table.parquet')
df.printSchema()
print(df.select("value"))
```

Output:

```
# Pyarrow reading enriched

id: int64
label: string
value: string
-- schema metadata --
org.apache.spark.version: '3.5.5'
org.apache.spark.sql.parquet.row.metadata: '{"type":"struct","fields":[{"' + 120
{b'org.apache.spark.version': b'3.5.5', b'org.apache.spark.sql.parquet.row.metadata': b'{"type":"struct","fields":[{"name":"id","type":"long","nullable":true,"metadata":{}},{"name":"label","type":"string","nullable":true,"metadata":{}}]}'}
# Pyspark reading enriched

root
|-- id: long (nullable = true)
|-- label: string (nullable = true)

Traceback (most recent call last):

pyspark.errors.exceptions.captured.AnalysisException: [UNRESOLVED_COLUMN.WITH_SUGGESTION] A column or function parameter with name `value` cannot be resolved. Did you mean one of the following? [`id`, `label`].;
'Project ['value]
+- Relation [id#6L,label#7] parquet

Process finished with exit code 1
```

This use case might look a bit convoluted, but in the whole context of the app, it makes sense.

### Component(s)

Parquet, Python