**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
except ApiException as e:
                    if e.reason == "BadRequest":
                        self.log.error("Request was invalid. Failing task")
                        key, _, _, _ = task
                        self.change_state(key, State.FAILED, e)
                    else:
                        self.log.warning(
                            'ApiException when attempting to run task, re-queueing. Message: %s',
                            json.loads(e.body)['message'],
                        )
                        self.task_queue.put(task)
                finally:
                    self.task_queue.task_done()
            except Empty:
                break
        
    def _change_state(self, key: TaskInstanceKey, state: Optional[str], pod_id: str, namespace: str) -> None:
        if state != State.RUNNING:
            if self.kube_config.delete_worker_pods:
                if not self.kube_scheduler:
                    raise AirflowException(NOT_STARTED_MESSAGE)
                if state != State.FAILED or self.kube_config.delete_worker_pods_on_failure:
                    self.kube_scheduler.delete_pod(pod_id, namespace)
                    self.log.info('Deleted pod: %s in namespace %s', str(key), str(namespace))
            try:
                self.running.remove(key)
            except KeyError:
                self.log.debug('Could not find key: %s', str(key))
        self.event_buffer[key] = state, None
    def try_adopt_task_instances(self, tis: List[TaskInstance]) -> List[TaskInstance]: