**Apache Airflow version**: 2.0.1
**Kubernetes version**: v1.19.7
**Executor**: KubernetesExecutor

**What happened**:

If you have a worker that gets stuck in pending forever, say with a missing volume mount, the task will stay in the queued state forever. Nothing is applying a timeout on it actually being able to start.

**What you expected to happen**:

Eventually the scheduler will notice that the worker hasn't progressed past pending after a given amount of time and will mark it as a failure.

**How to reproduce it**:
```python
from airflow import DAG
from airflow.operators.bash import BashOperator
from airflow.utils.dates import days_ago
from kubernetes.client import models as k8s


default_args = {
    "owner": "airflow",
}

with DAG(
    dag_id="pending",
    default_args=default_args,
    schedule_interval=None,
    start_date=days_ago(2),
) as dag:
    BashOperator(
        task_id="forever_pending",
        bash_command="date; sleep 30; date",
        executor_config={
            "pod_override": k8s.V1Pod(
                spec=k8s.V1PodSpec(
                    containers=[
                        k8s.V1Container(
                            name="base",
                            volume_mounts=[
                                k8s.V1VolumeMount(mount_path="/foo/", name="vol")
                            ],
                        )
                    ],
                    volumes=[
                        k8s.V1Volume(
                            name="vol",
                            persistent_volume_claim=k8s.V1PersistentVolumeClaimVolumeSource(
                                claim_name="missing"
                            ),
                        )
                    ],
                )
            ),
        },
    )
```


**Anything else we need to know**:
Related to:

* #15149 (This is reporting that these pending pods don't get deleted via "Mark Failed")
* #14556 (This handles when these pending pods get deleted and is already fixed)

airflow/executors/kubernetes_executor.py
'resource_version: %s',
                status,
                pod_id,
                namespace,
                annotations,
                resource_version,
            )
class AirflowKubernetesScheduler(LoggingMixin):
    """Airflow Scheduler for Kubernetes"""
    def __init__(
        self,
        kube_config: Any,
        task_queue: 'Queue[KubernetesJobType]',
        result_queue: 'Queue[KubernetesResultsType]',
        kube_client: client.CoreV1Api,
        scheduler_job_id: str,
    ):
        super().__init__()
        self.log.debug("Creating Kubernetes executor")
        self.kube_config = kube_config
        self.task_queue = task_queue
        self.result_queue = result_queue
        self.namespace = self.kube_config.kube_namespace
        self.log.debug("Kubernetes using namespace %s", self.namespace)
        self.kube_client = kube_client
        self._manager = multiprocessing.Manager()
        self.watcher_queue = self._manager.Queue()
        self.scheduler_job_id = scheduler_job_id
        self.kube_watcher = self._make_kube_watcher()
    def run_pod_async(self, pod: V1Pod, **kwargs):