fetch_record_batch is 2x/3x slower than raw pyArrow

Chưa có ai nhận issue này.

### What happens?

Hi Team,

First of all, thanks for the all the hard work on DuckDB, it's an amazing product.

From my testing, it seems that `DuckDB` slows down significantly when querying parquet data and serving the result as `record_batches`. I'm not super sure the exact issue but it's usually 2x/3x slower than polars/pyarrow.

You can reproduce it below with `uv run xx.py`
### To Reproduce

```python
# /// script
# requires-python = ">=3.13"
# dependencies = [
# "duckdb==1.4.4",
# "polars==1.38.1",
# "pyarrow==23.0.1",
# ]
# ///
import pyarrow.parquet as pq
import pyarrow as pa

import duckdb
import polars as pl
from itertools import permutations
import time
from contextlib import contextmanager

pa.show_info()

# Create parquet file with permutations of 'abcdef'
perms = list(permutations('abcdefghijk'))
print(len(perms))
data = {'permutation': [''.join(p) for p in perms]}
table = pa.table(data)
pq.write_table(table, 'alphabet_test.parquet')

time_taken = {}
@contextmanager
def timer(name):
start = time.time()
try:
yield
finally:
elapsed = time.time() - start
print(f"{name}: {elapsed:.6f}s")
time_taken[name] = elapsed

# Test DuckDB fetch_arrow
with timer("DuckDB fetch_arrow_table"):
reader = duckdb.read_parquet('alphabet_test.parquet').fetch_arrow_table(batch_size=100_000)

# Test DuckDB fetch_record_batch
with timer("DuckDB fetch_record_batch"):
reader = duckdb.read_parquet('alphabet_test.parquet').fetch_arrow_reader(batch_size=100_000)
batches_duckdb = []
for batch in reader:
batches_duckdb.append(batch)

# Test PyArrow RecordBatchReader
with timer("PyArrow RecordBatchReader"):
ds = pa.dataset.dataset('alphabet_test.parquet')
reader = pa.dataset.Scanner.from_dataset(ds, batch_size=100_000).to_reader()
batches_pyarrow = []
for batch in reader:
batches_pyarrow.append(batch)

# Test Polars + PyArrow RecordBatchReader
with timer("Polars + PyArrow RecordBatchReader"):
ds = pl.scan_parquet('alphabet_test.parquet').collect_batches(chunk_size=100_000)
reader = pa.RecordBatchReader.from_stream(ds)
batches_pyarrow = []
for batch in reader:
batches_pyarrow.append(batch)

print("\nSummary of time taken:")
for name, elapsed in time_taken.items():
print(f"{name}: {elapsed:.6f}s")
print(f"fetch ducdkb batches {time_taken['DuckDB fetch_record_batch'] / time_taken['PyArrow RecordBatchReader']:.2f} slower than PyArrow RecordBatchReader")
```

### OS:

Darwin arm64

### DuckDB Version:

1.4.4

### DuckDB Client:

python

### Hardware:

Apple M4

### Full Name:

Valentino Chen

### Affiliation:

Personal

### Did you include all relevant configuration (e.g., CPU architecture, Linux distribution) to reproduce the issue?

- [x] Yes, I have

### Did you include all code required to reproduce the issue?

- [x] Yes, I have

### Did you include all relevant data sets for reproducing the issue?

No - Other reason (please specify in the issue body)

Bắt đầu bằng cách chạy bản tái hiện được cung cấp với `uv run xx.py` trên các phiên bản DuckDB, PyArrow và Polars đã nêu, sau đó so sánh thời gian của `fetch_arrow_table`, `fetch_record_batch` và trình đọc bên ngoài. Theo dõi đường đi record-batch của Python để xác định sự chậm lại xảy ra ở đâu. Công việc được xem là hoàn tất khi nút thắt cổ chai được xử lý và benchmark cho thấy hiệu năng được cải thiện mà không thay đổi kết quả.

Do mô hình lập chỉ mục viết ra từ nội dung của issue.