### Apache Airflow version

main (development)

### What happened

_list_pods function uses kube list_namespaced_pod and list_pod_for_all_namespaces kube functions. Right now, these Kube functions will get the entire pod spec though we are interested in the pod metadata alone. This _list_pods is refered in clear_not_launched_queued_tasks. try_adopt_task_instances and _adopt_completed_pods functions.

When we run the airflow at large scale (with worker pods of more than > 500). The _list_pods function takes a significant amount of time (upto 15 - 30 seconds with 500 worker pods) due to unnecessary data transfer (V1PodList up to a few 10 MBs) and JSON deserialization overhead. This is blocking us from scaling the airflow to run at large scale

### What you think should happen instead

Request the Pod metadata instead of entire Pod payload. It will help to reduce significant network data transfer and JSON deserialization overhead.

### How to reproduce

I have reproduced the performance issue while running 500 concurrent jobs. Monitor  kubernetes_executor.clear_not_launched_queued_tasks.duration and kubernetes_executor.adopt_task_instances.duration metrics.

### Operating System

CentOS 6

### Versions of Apache Airflow Providers

apache-airflow-providers-cncf-kubernetes

### Deployment

Other Docker-based deployment

### Deployment details

Terraform based airflow deployment

### Anything else

_No response_

### 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)

ti.queue = task_queue
        session.flush()
        executor = self.kubernetes_executor
        executor.job_id = 1
        executor.kubernetes_queue = kubernetes_queue
        executor.kube_client = _client
        executor.clear_not_launched_queued_tasks(session=session)
        ti.refresh_from_db()
        assert ti.state == State.QUEUED
        _client.list_namespaced_pod.assert_called_once_with(
            namespace="default", label_selector="airflow-worker=1"
        )
    @pytest.mark.db_test
    def test_clear_not_launched_queued_tasks_mapped_task(self, dag_maker, session):
        """One mapped task has a launched pod - other does not."""
        def list_namespaced_pod(*args, **kwargs):
            return k8s.V1PodList(
                items=[
                    k8s.V1Pod(
                        metadata=k8s.V1ObjectMeta(
                            labels={
                                "role": "airflow-worker",
                                "dag_id": "test_clear",
                                "task_id": "bash",
                                "airflow-worker": 1,
                                "map_index": 0,
                                "run_id": "test",
                            },
                        ),
                        status=k8s.V1PodStatus(phase="Pending"),