**Apache Airflow version**: 2.0.0


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

**Environment**:

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

**What happened**:

I have the following DAG:

```py
from datetime import datetime, timedelta

from airflow import DAG
from airflow.operators.bash_operator import BashOperator

with DAG(
    dag_id="sla_trigger",
    schedule_interval="* * * * *",
    start_date=datetime(2020, 2, 3),
) as dag:
    BashOperator(
        task_id="bash_task",
        bash_command="sleep 30",
        sla=timedelta(seconds=2),
    )

```

And in my understanding this dag should result in SLA miss every time it is triggered (every minute). However, after few minutes of running I don't see any SLA misses...

**What you expected to happen**:

I expect to see SLA if task takes longer than expected.

**How to reproduce it**:

Use the dag from above.

**Anything else we need to know**:

N/A

airflow/jobs/scheduler_job.py
for dag_model in dag_models:
            try:
                dag = self.dagbag.get_dag(dag_model.dag_id, session=session)
            except SerializedDagNotFound:
                self.log.exception("DAG '%s' not found in serialized_dag table", dag_model.dag_id)
                continue
            dag_hash = self.dagbag.dags_hash.get(dag.dag_id)
            
            
            
            
            
            
            
            
            if (dag.dag_id, dag_model.next_dagrun) not in active_dagruns:
                dag.create_dagrun(
                    run_type=DagRunType.SCHEDULED,
                    execution_date=dag_model.next_dagrun,
                    start_date=timezone.utcnow(),
                    state=State.RUNNING,
                    external_trigger=False,
                    session=session,
                    dag_hash=dag_hash,
                    creating_job_id=self.id,
                )
        self._update_dag_next_dagruns(dag_models, session)
        
        
    def _update_dag_next_dagruns(self, dag_models: Iterable[DagModel], session: Session) -> None: