Arrow scan: a NULL row passes a pushed-down `<=` filter against a NaN constant

### What happens?

When a `WHERE b <= 'nan'::DOUBLE` filter is pushed into an Arrow scan, the scan returns rows where b is NULL.

The same predicate over same rows copied to a native table (correctly) excludes the null rows.

This *should* exclude the nulls (`NULL <= x` is false): The bug appears to be in the arrow filter_pushdown: disabling the optimizer/pushdowns returns the correct results.

### To Reproduce

```py
import duckdb
import pyarrow as pa

with duckdb.connect() as con:

print(f"duckdb {duckdb.__version__}, pyarrow {pa.__version__}", "source id:", con.execute("SELECT source_id FROM pragma_version()").fetchone()[0])

arrow_table = pa.table({"b": pa.array([1.0, float("nan"), None])})
con.register("arrow_t", arrow_table)

result_native = con.execute("create table native_t as select * from arrow_t;select b from native_t where b <= 'nan'::DOUBLE").fetchall()

result = con.execute("select b from arrow_t where b <= 'nan'::DOUBLE").fetchall()

con.execute("PRAGMA disable_optimizer;")
result_no_optimizer = con.execute("select b from arrow_t where b <= 'nan'::DOUBLE").fetchall()

print(f"{result_native=}")
print(f"{result=}")
print(f"{result_no_optimizer=}")
```

## Result: Note that "result" differs from others
>
> duckdb 1.6.0.dev379, pyarrow 25.0.1 source id: a00803f768
> result_native=[(1.0,), (nan,)]
> result=[(1.0,), (nan,), (None,)]
> result_no_optimizer=[(1.0,), (nan,)]

### OS:

Windows & Ubuntu WSL

### DuckDB Package Version:

1.5.5 and 1.6.0.dev379

### Python Version:

3.14

### Full Name:

Paul T

### Affiliation:

Iqmo

### What is the latest build you tested with? If possible, we recommend testing with the latest nightly build.

I have tested with a nightly build

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

Yes

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

- [x] Yes, I have

### Did you include all relevant configuration to reproduce the issue?

- [x] Yes, I have

Beginne mit der Python-Reproduktion unter Verwendung der registrierten PyArrow-Tabelle und vergleiche den Arrow-Scan mit den Ergebnissen von native-table und optimizer-disabled. Verfolge das Arrow-Filter-Pushdown für `b <= 'nan'::DOUBLE`; als erledigt gilt die Aufgabe, wenn die per Pushdown ausgeführte Abfrage die NULL-Zeile wie die anderen Ergebnisse ausschließt und ein Regressionstest diesen Fall abdeckt.

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