### 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)

pod_name = "my-pod-1"
        namespace = "my-namespace-2"
        mock_delete_namespace = mock.MagicMock()
        _client.return_value.delete_namespaced_pod = mock_delete_namespace
        # ApiException is raised because status is not 404
        _client.return_value.delete_namespaced_pod.side_effect = ApiException(status=400)
        kunetesExecutor()
        kube_executor.job_id = 1
        kube_executor.start()
        with pytest.raises(ApiException):
            kube_executor.kube_scheduler.delete_pod(pod_name, namespace)
            mock_delete_namespace.assert_called_with(pod_name, namespace, body=mock_client.V1DeleteOptions())
    @pytest.mark.db_test
    @pytest.mark.skipif(
        AirflowKubernetesScheduler is None, reason="kubernetes python package is not installed"
    )
    @mock.patch("airflow.providers.cncf.kubernetes.kube_client.get_kube_client")
    @mock.patch("airflow.providers.cncf.kubernetes.executors.kubernetes_executor_utils.client")
    @mock.patch("airflow.providers.cncf.kubernetes.executors.kubernetes_executor_utils.KubernetesJobWatcher")
    def test_delete_pod_404_not_raised(self, mock_watcher, mock_client, _client):