### 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)

)
                ]
            )
        _client = mock.MagicMock()
        _client.list_namespaced_pod.side_effect = list_namespaced_pod
        with dag_maker(dag_id="test_clear"):
            op = BashOperator.partial(task_id="bash").expand(bash_command=["echo 0", "echo 1"])
        dag_run = dag_maker.create_dagrun()
        ti0 = dag_run.get_task_instance(op.task_id, session, map_index=0)
        ti0.state = State.QUEUED
        ti0.queued_by_job_id = 1
        ti1 = dag_run.get_task_instance(op.task_id, session, map_index=1)
        ti1.state = State.QUEUED
        ti1.queued_by_job_id = 1
        session.flush()
        executor = self.kubernetes_executor
        executor.job_id = 1
        executor.kube_client = _client
        executor.clear_not_launched_queued_tasks(session=session)
        ti0.refresh_from_db()
        ti1.refresh_from_db()
        assert ti0.state == State.QUEUED
        assert ti1.state == State.SCHEDULED
        assert _client.list_namespaced_pod.call_count == 1
        _client.list_namespaced_pod.assert_called_with(
            namespace="default",
            label_selector="airflow-worker=1",
        )
    @pytest.mark.db_test
    def test_clear_not_launched_queued_tasks_not_launched_other_queue(