**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
"""
    Test if _process_task_instances puts a task instance into SKIPPED state if any of its
    upstream tasks are skipped according to TriggerRuleDep.
    """
    clear_db_runs()
    with DAG(
        dag_id='test_task_with_upstream_skip_dag', start_date=DEFAULT_DATE, schedule_interval=None
    ) as dag:
        dummy1 = DummyOperator(task_id='dummy1')
        dummy2 = DummyOperator(task_id="dummy2")
        dummy3 = DummyOperator(task_id="dummy3")
        [dummy1, dummy2] >> dummy3
    
    dag.clear()
    dr = dag.create_dagrun(run_type=DagRunType.MANUAL, state=State.RUNNING, execution_date=DEFAULT_DATE)
    assert dr is not None
    with create_session() as session:
        tis = {ti.task_id: ti for ti in dr.get_task_instances(session=session)}
        
        tis[dummy1.task_id].state = State.SKIPPED
        tis[dummy2.task_id].state = State.SUCCESS
        assert tis[dummy3.task_id].state == State.NONE
    
    
    with create_session() as session:
        tis = {ti.task_id: ti for ti in dr.get_task_instances(session=session)}
        assert tis[dummy1.task_id].state == State.SKIPPED