### 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)

"""
        Patch completed pods so that the KubernetesJobWatcher can delete them.
        :param kube_client: kubernetes client for speaking to kube API
        """
        if TYPE_CHECKING:
            assert self.scheduler_job_id
        new_worker_id_label = self._make_safe_label_value(self.scheduler_job_id)
        query_kwargs = {
            "field_selector": "status.phase=Succeeded",
            "label_selector": (
                "kubernetes_executor=True,"
                f"airflow-worker!={new_worker_id_label},{POD_EXECUTOR_DONE_KEY}!=True"
            ),
        }
        pod_list = self._list_pods(query_kwargs)
        for pod in pod_list:
            self.log.info("Attempting to adopt pod %s", pod.metadata.name)
            from kubernetes.client.rest import ApiException
            try:
                kube_client.patch_namespaced_pod(
                    name=pod.metadata.name,
                    namespace=pod.metadata.namespace,
                    body={"metadata": {"labels": {"airflow-worker": new_worker_id_label}}},
                )
            except ApiException as e:
                self.log.info("Failed to adopt pod %s. Reason: %s", pod.metadata.name, e)
            ti_id = annotations_to_key(pod.metadata.annotations)
            self.running.add(ti_id)
    def _flush_task_queue(self) -> None: