**Apache Airflow version**: 2.0.0rc3

**Kubernetes version (if you are using kubernetes)** (use `kubectl version`):

**Environment**:

- **Cloud provider or hardware configuration**: Linux
- **OS** (e.g. from /etc/os-release): Ubuntu
- **Kernel** (e.g. `uname -a`): 
- **Install tools**:
- **Others**:

**What happened**:

After shutting down the scheduler, while tasks were in running state, trying to restart the scheduler results in pk violations..

```[2020-12-15 22:43:29,673] {scheduler_job.py:1293} ERROR - Exception when executing SchedulerJob._run_scheduler_loop
Traceback (most recent call last):
  File "/home/jcoder/git/airflow_2.0/pyenv/lib/python3.7/site-packages/sqlalchemy/engine/base.py", line 1277, in _execute_context
    cursor, statement, parameters, context
  File "/home/jcoder/git/airflow_2.0/pyenv/lib/python3.7/site-packages/sqlalchemy/engine/default.py", line 593, in do_execute
    cursor.execute(statement, parameters)
psycopg2.errors.UniqueViolation: duplicate key value violates unique constraint "dag_run_dag_id_run_id_key"
DETAIL:  Key (dag_id, run_id)=(example_task_group, scheduled__2020-12-14T04:31:00+00:00) already exists.
```


**What you expected to happen**:

Scheduler restarts and picks up where it left off.

**How to reproduce it**:

Set example dag ( I used task_group) to schedule_interval `* * * * *` and start the scheduler and let it run for a few minutes. 
Shut down the scheduler
Attempt to restart the scheduler

**Anything else we need to know**:
I came across this doing testing using the LocalExecutor in a virtual env. If no else is able to reproduce it, I'll try again in a clean virtual env.

tests/jobs/test_scheduler_job.py
dag_id = 'SchedulerJobTest.test_nonexistent_pool'
        task_id = 'dummy_wrong_pool'
        dag = DAG(dag_id=dag_id, start_date=DEFAULT_DATE, concurrency=16)
        task = DummyOperator(dag=dag, task_id=task_id, pool="this_pool_doesnt_exist")
        dag = SerializedDAG.from_dict(SerializedDAG.to_dict(dag))
        scheduler = SchedulerJob(subdir=os.devnull)
        session = settings.Session()
        dag_model = DagModel(
            dag_id=dag_id,
            is_paused=False,
            concurrency=dag.concurrency,
            has_task_concurrency_limits=False,
        )
        session.add(dag_model)
        dr = dag.create_dagrun(
            run_type=DagRunType.SCHEDULED,
            execution_date=DEFAULT_DATE,
            state=State.RUNNING,
        )
        ti = TaskInstance(task, dr.execution_date)
        ti.state = State.SCHEDULED
        session.merge(ti)
        session.commit()
        res = scheduler._executable_task_instances_to_queued(max_tis=32, session=session)
        session.flush()
        assert 0 == len(res)
        session.rollback()
    def test_find_executable_task_instances_none(self):