KubernetesExecutor: Task stuck in queued state when using deprecated `executor_config` dict format

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

Providers

### Apache Airflow version

3.3

### What happened and how to reproduce it?

When we try to run a dag with `executor_config={"KubernetesExecutor": {"config_file": "/some/path/kubeconfig.yaml"}}`, the task gets stuck in a `Queued` state on the UI for a long time and often takes over 40 minutes to fail. Further inspecting the scheduler logs shows the below:
```
→ export KUBECONFIG=/Users/81045729/Documents/constant_variables/airflow/.build/.k8s-clusters/airflow-python-3.10-v1.30.13/.kubeconfig && kubectl logs -n airflow airflow-scheduler-58dcc9f6cf-qnbvj -c scheduler 2>&1 | grep -i "Invalid executor_config\|TypeError\|error.*executor\|execute_async\|run_next\|Cannot convert\|failed\|Error\|Failed" | tail -200000
2026-05-28T12:18:23.173735Z [error ] Invalid executor_config for TaskInstanceKey(dag_id='test_k8s_executor_config_legacy', task_id='check_for_failure', run_id='manual__2026-05-28T12:18:22.589053+00:00', try_number=1, map_index=-1). Executor_config: {'KubernetesExecutor': {'config_file': '/some/path/kubeconfig.yaml'}} [airflow.providers.cncf.kubernetes.executors.kubernetes_executor.KubernetesExecutor] loc=kubernetes_executor.py:221
2026-05-28T12:18:23.174607Z [info ] Received executor event with state failed for task instance TaskInstanceKey(dag_id='test_k8s_executor_config_legacy', task_id='check_for_failure', run_id='manual__2026-05-28T12:18:22.589053+00:00', try_number=1, map_index=-1) [airflow.jobs.scheduler_job_runner.SchedulerJobRunner] loc=scheduler_job_runner.py:1251
2026-05-28T12:18:23.179662Z [info ] TaskInstance Finished: dag_id=test_k8s_executor_config_legacy, task_id=check_for_failure, run_id=manual__2026-05-28T12:18:22.589053+00:00, map_index=-1, ti_id=019e6e85-7d17-7605-aac9-7506f0cea68b, run_start_date=None, run_end_date=None, run_duration=None, state=queued, executor=KubernetesExecutor(parallelism=32), executor_state=failed, try_number=1, max_tries=0, pool=default_pool, queue=default, priority_weight=1, operator=PythonOperator, queued_dttm=2026-05-28 12:18:23.168826+00:00, scheduled_dttm=2026-05-28 12:18:23.158209+00:00,queued_by_job_id=3, pid=None [airflow.jobs.scheduler_job_runner.SchedulerJobRunner] loc=scheduler_job_runner.py:1340
```

### What you think should happen instead?

Since the `TaskInstance Finished` is reported by the scheduler almost instantly after triggering the DAG, the task should fail immediately on the UI, if possible, with a message to switch to using `pod_override` attribute inside `executor_config`.

### Operating System

MacOS

### Deployment

Virtualenv installation

### Apache Airflow Provider(s)

cncf-kubernetes

### Versions of Apache Airflow Providers

Issue not restricted to a specific provider version

### Official Helm Chart version

Not Applicable

### Kubernetes Version

_No response_

### Helm Chart configuration

_No response_

### Docker Image customizations

_No response_

### Anything else?

To reproduce, you can create the below dag, place it in `airflow-core/src/airflow/example_dags/`, and deploy the airflow server with `KubernetesExecutor` as mentioned in [contribution docs](https://github.com/kunaljubce/airflow/blob/main/contributing-docs/testing/k8s_tests.rst#typical-testing-pattern-for-kubernetes-tests):

```
from airflow.providers.standard.operators.python import PythonOperator
from airflow.sdk import DAG
import pendulum

def useless_func():
pass

with DAG(
dag_id = "test_k8s_executor_config_legacy",
schedule = None,
start_date=pendulum.datetime(2021, 1, 1, tz="UTC"),
catchup=False,
) as dag:

run_this = PythonOperator(
task_id = "check_for_failure",
python_callable = useless_func,
executor_config={"KubernetesExecutor": {"config_file": "/some/path/kubeconfig.yaml"}}
)

run_this
```

Once all commands have executed successfully, you should see the URL for the airflow UI in the console logs, looking like below (your port might be different to mine, verify from console logs):
```
KinD Cluster API server URL: http://localhost:44498
Connecting to localhost:34814. Num try: 1
Error when connecting to localhost:34814 : ('Connection aborted.', RemoteDisconnected('Remote end closed connection without response'))
Sleeping for 5 seconds.
Connecting to localhost:34814. Num try: 2
Error when connecting to localhost:34814 : ('Connection aborted.', RemoteDisconnected('Remote end closed connection without response'))
Sleeping for 5 seconds.
Connecting to localhost:34814. Num try: 3
Established connection to api server at http://localhost:34814/api/v2/monitor/health and it is healthy.
Airflow API server URL: http://localhost:34814 (admin/admin) <<<<---- Airflow UI URL

NEXT STEP: You might now run tests or interact with airflow via shell (kubectl, pytest etc.) or k9s commands:
```

Copy and paste the URL on your browser, trigger the DAG and watch it go into queued state

### Are you willing to submit PR?

- [x] 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)