**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)

tests/executors/test_kubernetes_executor.py
mock_api_client.sanitize_for_serialization.return_value = {}
        mock_kube_client.api_client = mock_api_client
        config = {
            ('kubernetes', 'pod_template_file'): path,
        }
        with conf_vars(config):
            kubernetes_executor = self.kubernetes_executor
            kubernetes_executor.start()
            
            try_number = 1
            kubernetes_executor.execute_async(
                key=('dag', 'task', datetime.utcnow(), try_number),
                queue=None,
                command=['airflow', 'tasks', 'run', 'true', 'some_parameter'],
            )
            kubernetes_executor.sync()
            kubernetes_executor.sync()
            assert mock_kube_client.create_namespaced_pod.called
            assert not kubernetes_executor.task_queue.empty()
            
            mock_kube_client.create_namespaced_pod.side_effect = None
            
            kubernetes_executor.sync()
            assert mock_kube_client.create_namespaced_pod.called
            assert kubernetes_executor.task_queue.empty()
    @mock.patch('airflow.executors.kubernetes_executor.KubeConfig')
    @mock.patch('airflow.executors.kubernetes_executor.KubernetesExecutor.sync')
    @mock.patch('airflow.executors.base_executor.BaseExecutor.trigger_tasks')
    @mock.patch('airflow.executors.base_executor.Stats.gauge')
    def test_gauge_executor_metrics(self, mock_stats_gauge, mock_trigger_tasks, mock_sync, mock_kube_config):