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ORDER BY is ignored when COPYing from a pyarrow table to a csv file #609

Description

@progval

Describe the bug
ORDER BY is ignored when COPYing from a pyarrow table to a csv file

This happens both for tables created with pyarrow.Table.from_pydict and from ORC files.

To Reproduce

with datafusion 35.0.0 installed from PyPI:

from pathlib import Path

import datafusion
import pyarrow.csv
import pyarrow.dataset

ctx = datafusion.SessionContext()
ctx.from_arrow_table(pyarrow.Table.from_pydict({'value': [2, 1, 3]}), "content")

output_path = Path("/tmp/output.csv")

query = f"""
    COPY (SELECT value FROM content ORDER BY value DESC)
    TO '{output_path}' (
        FORMAT CSV,
    )
"""
df = ctx.sql(query)

columns = df.schema().names
assert columns == ["value"], columns

df.count()  # force the query to run

print(output_path.read_text())
$ python3 /tmp/order_arrow_table.py
value
2
1
3

Expected behavior
Should print

value
1
2
3

Additional context

I tried to reproduce it directly in Rust (apache/datafusion#9463), but this code does produce a sorted output as expected:

use std::sync::Arc;
use datafusion::arrow::array::PrimitiveArray;
use datafusion::arrow::datatypes::{DataType, Field, Schema, Int64Type};
use datafusion::arrow::record_batch::RecordBatch;
use datafusion::prelude::*;
use datafusion::datasource::MemTable;

#[tokio::main]
async fn main() {
    let ctx = SessionContext::new();

    let schema = Arc::new(Schema::new(vec![Field::new("value", DataType::Int64, false)]));
    let column: PrimitiveArray<Int64Type> = vec![2, 1, 3].into();
    let partition = RecordBatch::try_new(schema.clone(), vec![Arc::new(column)]).unwrap();
    let table = MemTable::try_new(schema, vec![vec![partition]]).unwrap();
    ctx.register_table("content", Arc::new(table)).unwrap();

    let df = ctx.sql("COPY (SELECT value FROM content ORDER BY value) TO '/tmp/output.csv'").await.unwrap();
    df.collect().await.unwrap();
}

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