**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
open_slots,
                num_ready,
            )
            priority_sorted_task_instances = sorted(
                task_instances, key=lambda ti: (-ti.priority_weight, ti.execution_date)
            )
            num_starving_tasks = 0
            for current_index, task_instance in enumerate(priority_sorted_task_instances):
                if open_slots <= 0:
                    self.log.info("Not scheduling since there are %s open slots in pool %s", open_slots, pool)
                    
                    num_unhandled = len(priority_sorted_task_instances) - current_index
                    num_starving_tasks += num_unhandled
                    num_starving_tasks_total += num_unhandled
                    break
                
                
                dag_id = task_instance.dag_id
                current_dag_concurrency = dag_concurrency_map[dag_id]
                dag_concurrency_limit = task_instance.dag_model.concurrency
                self.log.info(
                    "DAG %s has %s/%s running and queued tasks",
                    dag_id,
                    current_dag_concurrency,
                    dag_concurrency_limit,
                )
                if current_dag_concurrency >= dag_concurrency_limit:
                    self.log.info(
                        "Not executing %s since the number of tasks running or queued "
                        "from DAG %s is >= to the DAG's task concurrency limit of %s",