### 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)

for ti in queued_tis:
                self.log.debug("Checking task instance %s", ti)
                
                if ti.key in self.last_handled:
                    continue
                
                base_label_selector = (
                    f"dag_id={self._make_safe_label_value(ti.dag_id)},"
                    f"task_id={self._make_safe_label_value(ti.task_id)},"
                    f"airflow-worker={self._make_safe_label_value(str(ti.queued_by_job_id))}"
                )
                if ti.map_index >= 0:
                    
                    base_label_selector += f",map_index={ti.map_index}"
                
                label_search_str = f"{base_label_selector},run_id={self._make_safe_label_value(ti.run_id)}"
                if label_search_str in label_search_set:
                    continue
                
                label_search_str = (
                    f"{base_label_selector},execution_date={self._make_safe_label_value(ti.execution_date)}"
                )
                if label_search_str in label_search_set:
                    continue
                self.log.info("TaskInstance: %s found in queued state but was not launched, rescheduling", ti)
                session.execute(
                    update(TaskInstance)
                    .where(
                        TaskInstance.dag_id == ti.dag_id,
                        TaskInstance.task_id == ti.task_id,