### 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)

"""fetch task log from pod"""
        _client = mock_get_kube_client.return_value
        _client.read_namespaced_pod_log.return_value = [b"a_", b"b_", b"c_"]
        mock_pod = mock.Mock()
        mock_pod.metadata.name = "x"
        _client.list_namespaced_pod.return_value.items = [mock_pod]
        ti = create_task_instance_of_operator(EmptyOperator, dag_id="test_k8s_log_dag", task_id="test_task")
        executor = KubernetesExecutor()
        messages, logs = executor.get_task_log(ti=ti, try_number=1)
        _client.read_namespaced_pod_log.assert_called_once()
        assert messages == [
            "Attempting to fetch logs from pod  through kube API",
            "Found logs through kube API",
        ]
        assert logs[0] == "a_\nb_\nc_"
        _client.reset_mock()
        _client.read_namespaced_pod_log.side_effect = Exception("error_fetching_pod_log")
        messages, logs = executor.get_task_log(ti=ti, try_number=1)
        assert logs == [""]
        assert messages == [
            "Attempting to fetch logs from pod  through kube API",
            "Reading from k8s pod logs failed: error_fetching_pod_log",
        ]
    def test_supports_pickling(self):