**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/jobs/scheduler_job.py
dag = dagbag.dags[request.dag_id]
        dag_run = dag.get_dagrun(execution_date=request.execution_date, session=session)
        dag.handle_callback(
            dagrun=dag_run, success=not request.is_failure_callback, reason=request.msg, session=session
        )
    def _execute_task_callbacks(self, dagbag: DagBag, request: TaskCallbackRequest):
        simple_ti = request.simple_task_instance
        if simple_ti.dag_id in dagbag.dags:
            dag = dagbag.dags[simple_ti.dag_id]
            if simple_ti.task_id in dag.task_ids:
                task = dag.get_task(simple_ti.task_id)
                ti = TI(task, simple_ti.execution_date)
                
                ti.start_date = simple_ti.start_date
                ti.end_date = simple_ti.end_date
                ti.try_number = simple_ti.try_number
                ti.state = simple_ti.state
                ti.test_mode = self.UNIT_TEST_MODE
                if request.is_failure_callback:
                    ti.handle_failure_with_callback(error=request.msg, test_mode=ti.test_mode)
                    self.log.info('Executed failure callback for %s in state %s', ti, ti.state)
    @provide_session
    def process_file(