**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
tis_to_flush = [ti for ti in tis if not ti.external_executor_id]
        scheduler_job_ids = [ti.external_executor_id for ti in tis]
        pod_ids = {
            create_pod_id(
                dag_id=pod_generator.make_safe_label_value(ti.dag_id),
                task_id=pod_generator.make_safe_label_value(ti.task_id),
            ): ti
            for ti in tis
            if ti.external_executor_id
        }
        kube_client: client.CoreV1Api = self.kube_client
        for scheduler_job_id in scheduler_job_ids:
            scheduler_job_id = pod_generator.make_safe_label_value(str(scheduler_job_id))
            kwargs = {'label_selector': f'airflow-worker={scheduler_job_id}'}
            pod_list = kube_client.list_namespaced_pod(namespace=self.kube_config.kube_namespace, **kwargs)
            for pod in pod_list.items:
                self.adopt_launched_task(kube_client, pod, pod_ids)
        self._adopt_completed_pods(kube_client)
        tis_to_flush.extend(pod_ids.values())
        return tis_to_flush
    def adopt_launched_task(self, kube_client, pod, pod_ids: dict):