### 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)

self,
        key: TaskInstanceKey,
        command: CommandType,
        queue: str | None = None,
        executor_config: Any | None = None,
    ) -> None:
        """Execute task asynchronously."""
        if TYPE_CHECKING:
            assert self.task_queue
        if self.log.isEnabledFor(logging.DEBUG):
            self.log.debug("Add task %s with command %s, executor_config %s", key, command, executor_config)
        else:
            self.log.info("Add task %s with command %s", key, command)
        from airflow.providers.cncf.kubernetes.pod_generator import PodGenerator
        try:
            kube_executor_config = PodGenerator.from_obj(executor_config)
        except Exception:
            self.log.error("Invalid executor_config for %s. Executor_config: %s", key, executor_config)
            self.fail(key=key, info="Invalid executor_config passed")
            return
        if executor_config: