### 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)

"""
        We should not adopt any tasks that were not assigned by the scheduler.
        This ensures that there is no contention over pod management.
        """
        executor = self.kubernetes_executor
        executor.scheduler_job_id = "modified"
        tis_to_flush_by_key = {"foobar": {}}
        pod = k8s.V1Pod(
            metadata=k8s.V1ObjectMeta(
                name="foo",
                labels={"airflow-worker": "bar"},
                annotations={
                    "dag_id": "dag",
                    "run_id": "run_id",
                    "task_id": "task",
                    "try_number": "1",
                },
            )
        )
        executor.adopt_launched_task(_client, pod=pod, tis_to_flush_by_key=tis_to_flush_by_key)
        assert not _client.patch_namespaced_pod.called
        assert tis_to_flush_by_key == {"foobar": {}}
    @pytest.mark.db_test
    @mock.patch("airflow.providers.cncf.kubernetes.kube_client.get_kube_client")
    @mock.patch(
        "airflow.providers.cncf.kubernetes.executors.kubernetes_executor_utils.AirflowKubernetesScheduler.delete_pod"
    )
    def test_cleanup_stuck_queued_tasks(self, mock_delete_pod, _client, dag_maker, session):