### 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)

test_namespace = "airflow"
    def setup_method(self):
        self.watcher = KubernetesJobWatcher(
            namespace=self.test_namespace,
            watcher_queue=mock.MagicMock(),
            resource_version="0",
            scheduler_job_id="123",
            kube_config=mock.MagicMock(),
        )
        self.kube_client = mock.MagicMock()
        self.core_annotations = {
            "dag_id": "dag",
            "task_id": "task",
            "run_id": "run_id",
            "try_number": "1",
            "execution_date": None,
        }
        self.pod = k8s.V1Pod(
            metadata=k8s.V1ObjectMeta(
                name="foo",
                annotations={"airflow-worker": "bar", **self.core_annotations},
                namespace="airflow",
                resource_version="456",
                labels={},
            ),
            status=k8s.V1PodStatus(phase="Pending"),
        )
        self.events = []
    def _run(self):