### 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)

regex = r"^[a-z0-9]([-a-z0-9]*[a-z0-9])?(\.[a-z0-9]([-a-z0-9]*[a-z0-9])?)*$"
        return len(name) <= 253 and name.islower() and re.match(regex, name)
    @staticmethod
    def _is_safe_label_value(value):
        regex = r"^[^a-z0-9A-Z]*|[^a-zA-Z0-9_\-\.]|[^a-z0-9A-Z]*$"
        return len(value) <= 63 and re.match(regex, value)
    @pytest.mark.skipif(
        AirflowKubernetesScheduler is None, reason="kubernetes python package is not installed"
    )
    def test_create_pod_id(self):
        for dag_id, task_id in self._cases():
            pod_name = PodGenerator.make_unique_pod_id(create_pod_id(dag_id, task_id))
            assert self._is_valid_pod_id(pod_name)
    @pytest.mark.skipif(
        AirflowKubernetesScheduler is None, reason="kubernetes python package is not installed"
    )
    @mock.patch("airflow.providers.cncf.kubernetes.pod_generator.PodGenerator")
    @mock.patch("airflow.providers.cncf.kubernetes.executors.kubernetes_executor.KubeConfig")
    def test_get_base_pod_from_template(self, config, mock_generator):