### Apache Airflow version

2.7.0rc2

### What happened

In the Airflow UI, currently, the DAG Processor is visible in the Cluster Activity page even if there is no stand-alone dag processor. 

### What you think should happen instead

It should be hidden if there is no stand-alone dag processor.

### How to reproduce

Run Airflow 2.7 

### Operating System

Mac OS

### Versions of Apache Airflow Providers

Airflow > 2.7

### Deployment

Official Apache Airflow Helm Chart

### Deployment details

_No response_

### Anything else

_No response_

### Are you willing to submit PR?

- [X] Yes I am willing to submit a PR!

### Code of Conduct

- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

failed_task_instance.dag_id,
                failed_task_instance.task_id,
                failed_task_instance.run_id,
            )
            if failed_task_instance.duration:
                fails_totals[dict_key] += failed_task_instance.duration
        
        def grouping_key(ti: TaskInstance):
            return ti.dag_id, ti.task_id, ti.run_id
        mapped_tis = set()
        for _, group in itertools.groupby(sorted(task_instances, key=grouping_key), key=grouping_key):
            tis = list(group)
            duration = sum(x.duration for x in tis if x.duration)
            if duration:
                first_ti = tis[0]
                if first_ti.map_index >= 0:
                    mapped_tis.add(first_ti.task_id)
                date_time = wwwutils.epoch(first_ti.execution_date)
                x_points[first_ti.task_id].append(date_time)
                fails_dict_key = (first_ti.dag_id, first_ti.task_id, first_ti.run_id)
                fails_total = fails_totals[fails_dict_key]
                y_points[first_ti.task_id].append(float(duration + fails_total))
        cumulative_y = {k: list(itertools.accumulate(v)) for k, v in y_points.items()}
        
        
        y_unit = infer_time_unit([d for t in y_points.values() for d in t])
        cum_y_unit = infer_time_unit([d for t in cumulative_y.values() for d in t])
        
        chart.create_y_axis("yAxis", format=".02f", custom_format=False, label=f"Duration ({y_unit})")
        chart.axislist["yAxis"]["axisLabelDistance"] = "-15"