### 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)

try:
                        self._change_state(key, state, pod_name, namespace)
                    except Exception as e:
                        self.log.exception(
                            "Exception: %s when attempting to change state of %s to %s, re-queueing.",
                            e,
                            results,
                            state,
                        )
                        self.result_queue.put(results)
                finally:
                    self.result_queue.task_done()
        from airflow.providers.cncf.kubernetes.executors.kubernetes_executor_utils import ResourceVersion
        resource_instance = ResourceVersion()
        for ns in resource_instance.resource_version:
            resource_instance.resource_version[ns] = (
                last_resource_version[ns] or resource_instance.resource_version[ns]
            )
        from kubernetes.client.rest import ApiException
        with contextlib.suppress(Empty):
            for _ in range(self.kube_config.worker_pods_creation_batch_size):
                task = self.task_queue.get_nowait()
                try:
                    self.kube_scheduler.run_next(task)
                except PodReconciliationError as e:
                    self.log.error(
                        "Pod reconciliation failed, likely due to kubernetes library upgrade. "
                        "Try clearing the task to re-run.",
                        exc_info=True,
                    )