### 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)

current_folder = pathlib.Path(__file__).parent.resolve()
        template_file = str(
            (current_folder / "kubernetes_executor_template_files" / "basic_template.yaml").resolve()
        )
        _client = mock.patch("kubernetes.client.CoreV1Api", autospec=True)
        mock_get_kube_client.return_value = _client
        with conf_vars({("kubernetes", "pod_template_file"): ""}):
            executor = self.kubernetes_executor
            executor.start()
            try:
                assert executor.event_buffer == {}
                assert executor.task_queue.empty()
                executor.execute_async(
                    key=TaskInstanceKey("dag", "task", "run_id", 1),
                    queue=None,
                    command=["airflow", "tasks", "run", "true", "some_parameter"],
                    executor_config={
                        "pod_template_file": template_file,
                        "pod_override": k8s.V1Pod(
                            metadata=k8s.V1ObjectMeta(labels={"release": "stable"}),