### 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)

)
        resettry_adopt_task_instances([mock_ti])
        _client.list_namespaced_pod.assert_called_once_with(
            namespace="default",
            field_selector="status.phase!=Succeeded",
            label_selector="kubernetes_executor=True,airflow-worker=10,airflow_executor_done!=True",
        )
        mock_adopt_launched_task.assert_called_once()  # Won't check args this time around as they get mutated
        mock_adopt_completed_pods.assert_called_once()
        assert reset_tis == []  # This time our return is empty - no TIs to reset
    @mock.patch(
        "airflow.providers.cncf.kubernetes.executors.kubernetes_executor.KubernetesExecutor._adopt_completed_pods"
    )
    def test_try_adopt_task_instances_multiple_scheduler_ids(self, mock_adopt_completed_pods):
        """We try to find pods only once per scheduler id"""
        executor = self.kubernetes_executor
        _client = mock.MagicMock()
        executor.kube_client = _client
        mock_tis = [
            mock.MagicMock(queued_by_job_id="10", external_executor_id="1", dag_id="dag", task_id="task"),
            mock.MagicMock(queued_by_job_id="40", external_executor_id="1", dag_id="dag", task_id="task2"),
            mock.MagicMock(queued_by_job_id="40", external_executor_id="1", dag_id="dag", task_id="task3"),
        ]
        executor.try_adopt_task_instances(mock_tis)
        assert _client.list_namespaced_pod.call_count == 2
        _client.list_namespaced_pod.assert_has_calls(
            [
                mock.call(
                    namespace="default",
                    field_selector="status.phase!=Succeeded",