PythonVirtualenvOperator hangs indefinitely on Variable.get() (Task SDK) under airflow dags test and dag.test() — no error, no timeout

### Under which category would you file this issue?

Airflow Core

### Apache Airflow version

3.2.0

### What happened and how to reproduce it?

When running a DAG that uses `PythonVirtualenvOperator` whose callable calls `from airflow.sdk import Variable; Variable.get(...)`, the venv subprocess hangs forever as soon as `Variable.get()` is called. There is no error, no timeout, no traceback — the `airflow dags test / dag.test()` invocation simply never returns.
The hang reproduces:
- under `dag.test()` (in-process execution)
- under `airflow dags test ` with a separately running airflow api-server reachable via `AIRFLOW__CORE__EXECUTION_API_SERVER_URL`

In both cases the last log line from the venv is the user's own `print("Python inside virtualenv: 3.10.17 ...")` — i.e. the subprocess is alive, the callable has started, and execution blocks on the very next line which is Variable.get(...).

Versions:
- apache-airflow-core==3.2.0
- apache-airflow-providers-standard==1.12.3
- apache-airflow-task-sdk==1.2.0
- Python 3.10.17 (host interpreter and venv interpreter)
- SQLite metadata DB
- OS: Linux

**How to reproduce**
Minimal script (single file, runs end-to-end):
```
import json
import os
import shutil
import subprocess
import sys
from datetime import datetime, timezone
from pathlib import Path

AIRFLOW_HOME = Path("/tmp/airflow32_venv_hang")
AIRFLOW_DB = AIRFLOW_HOME / "airflow.db"
DAG_BUNDLE_DIR = AIRFLOW_HOME / "dags_bundle"
AIRFLOW_HOME.mkdir(parents=True, exist_ok=True)
DAG_BUNDLE_DIR.mkdir(parents=True, exist_ok=True)

_THIS_FILE = Path(__file__).resolve()
_BUNDLE_FILE = (DAG_BUNDLE_DIR / "venv_hang_repro.py").resolve()
if _THIS_FILE != _BUNDLE_FILE:
shutil.copy(_THIS_FILE, _BUNDLE_FILE)

os.environ["AIRFLOW_HOME"] = str(AIRFLOW_HOME)
os.environ["AIRFLOW__DATABASE__SQL_ALCHEMY_CONN"] = f"sqlite:///{AIRFLOW_DB}"
os.environ["AIRFLOW__CORE__LOAD_EXAMPLES"] = "False"
os.environ["AIRFLOW__DAG_PROCESSOR__DAG_BUNDLE_CONFIG_LIST"] = json.dumps([{
"name": "dags-folder",
"classpath": "airflow.dag_processing.bundles.local.LocalDagBundle",
"kwargs": {"path": str(DAG_BUNDLE_DIR), "refresh_interval": 0},
}])
os.environ["AIRFLOW_VAR_DEMO_MESSAGE"] = "hello from env"

from airflow.sdk import DAG
from airflow.providers.standard.operators.python import PythonVirtualenvOperator

def read_variable_in_venv():
import sys
from airflow.sdk import Variable
print(f"Python inside virtualenv: {sys.version}", flush=True)
# HANGS HERE FOREVER, no error, no timeout:
value = Variable.get("demo_message", default="default value")
print(f"demo_message = {value}", flush=True)

with DAG(
dag_id="venv_hang_repro",
start_date=datetime(2024, 1, 1, tzinfo=timezone.utc),
schedule=None,
catchup=False,
) as dag:
PythonVirtualenvOperator(
task_id="read_variable_from_airflow",
python_callable=read_variable_in_venv,
python_version="3.10",
requirements=[
"apache-airflow==3.2.0",
],
system_site_packages=True,
)

if __name__ == "__main__":
subprocess.run([sys.executable, "-m", "airflow", "db", "migrate"], check=True)
subprocess.run([sys.executable, "-m", "airflow", "dags", "reserialize"], check=True)
dag.test()
```
Run: python repro.py. Observed behaviour: last log line is Python inside virtualenv: 3.10.17 ..., then the process hangs indefinitely and has to be killed with Ctrl-C.

### What you think should happen instead?

One of:
- Variable.get() inside a venv subprocess should work (return the value / default), or
- fail fast with a clear error.

### Operating System

_No response_

### Deployment

None

### Apache Airflow Provider(s)

_No response_

### Versions of Apache Airflow Providers

_No response_

### Official Helm Chart version

Not Applicable

### Kubernetes Version

_No response_

### Helm Chart configuration

_No response_

### Docker Image customizations

_No response_

### Anything else?

_No response_

### Are you willing to submit PR?

- [ ] Yes I am willing to submit a PR!

### Code of Conduct

- [x] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)