### 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)

with Stats.timer("kubernetes_executor.adopt_task_instances.duration"):
            
            tis_to_flush = [ti for ti in tis if not ti.queued_by_job_id]
            scheduler_job_ids = {ti.queued_by_job_id for ti in tis}
            tis_to_flush_by_key = {ti.key: ti for ti in tis if ti.queued_by_job_id}
            kube_client: client.CoreV1Api = self.kube_client
            for scheduler_job_id in scheduler_job_ids:
                scheduler_job_id = self._make_safe_label_value(str(scheduler_job_id))
                
                
                
                
                
                query_kwargs = {
                    "field_selector": "status.phase!=Succeeded",
                    "label_selector": (
                        "kubernetes_executor=True,"
                        f"airflow-worker={scheduler_job_id},{POD_EXECUTOR_DONE_KEY}!=True"
                    ),
                }
                pod_list = self._list_pods(query_kwargs)
                for pod in pod_list:
                    self.adopt_launched_task(kube_client, pod, tis_to_flush_by_key)
            self._adopt_completed_pods(kube_client)
            tis_to_flush.extend(tis_to_flush_by_key.values())
            return tis_to_flush
    def cleanup_stuck_queued_tasks(self, tis: list[TaskInstance]) -> list[str]: