### 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 shouldn't think we are running the task if aren't able to patch the pod"""
        executor = self.kubernetes_executor
        executor.scheduler_job_id = "modified"
        annotations = {
            "dag_id": "dag",
            "run_id": "run_id",
            "task_id": "task",
            "try_number": "1",
        }
        ti_key = annotations_to_key(annotations)
        pod = k8s.V1Pod(metadata=k8s.V1ObjectMeta(name="foo", annotations=annotations))
        tis_to_flush_by_key = {ti_key: {}}
        _client.patch_namespaced_pod.side_effect = ApiException(status=400)
        executor.adopt_launched_task(_client, pod=pod, tis_to_flush_by_key=tis_to_flush_by_key)
        _client.patch_namespaced_pod.assert_called_once_with(
            body={"metadata": {"labels": {"airflow-worker": "modified"}}},
            name="foo",
            namespace=None,
        )
        assert tis_to_flush_by_key == {ti_key: {}}
        assert executor.running == set()
    @mock.patch("airflow.providers.cncf.kubernetes.kube_client.get_kube_client")
    def test_adopt_completed_pods(self, _client):