Normalize count metric type in assert_metrics_using_metadata()

[florimondmanca]

What does this PR do?

Allow monotonic_count metrics to match expected type of count when comparing collected metrics with metadata.csv.

Motivation

metadata.csv cannot contain monotonic_count (only count is allowed AFAIK), so when a monotonic_count metric is submitted and is defined in metadata.csv, assert_metrics_using_metadata() fails.

Example for Azure IoT Edge (currently making #7465 fail):

tests/test_check.py:58: in test_check
    aggregator.assert_metrics_using_metadata(get_metadata_metrics())
../datadog_checks_base/datadog_checks/base/stubs/aggregator.py:358: in assert_metrics_using_metadata
    assert not errors, "Metadata assertion errors using metadata.csv:" + "\n\t- ".join([''] + sorted(errors))
E   AssertionError: Metadata assertion errors using metadata.csv:
E       - Expect `azure.iot_edge.edge_agent.iothub_syncs_total` to have type `count` but got `monotonic_count`.
E       - Expect `azure.iot_edge.edge_agent.module_start_total` to have type `count` but got `monotonic_count`.
E       - Expect `azure.iot_edge.edge_agent.module_stop_total` to have type `count` but got `monotonic_count`.
E       - Expect `azure.iot_edge.edge_agent.unsuccessful_iothub_syncs_total` to have type `count` but got `monotonic_count`.
E       - Expect `azure.iot_edge.edge_hub.client_connect_failed_total` to have type `count` but got `monotonic_count`.

Additional Notes

Review checklist (to be filled by reviewers)

• Feature or bugfix MUST have appropriate tests (unit, integration, e2e)
• PR title must be written as a CHANGELOG entry (see why)
• Files changes must correspond to the primary purpose of the PR as described in the title (small unrelated changes should have their own PR)
• PR must have changelog/ and integration/ labels attached

[codecov]

Codecov Report

Merging #7666 into master will decrease coverage by 0.36%.
The diff coverage is 0.00%.

Impacted Files	Coverage Δ
...hecks_base/datadog_checks/base/stubs/aggregator.py	66.40% <0.00%> (-0.54%)	⬇️
datadog_checks_base/tests/test_pdhbasecheck.py	36.25% <0.00%> (-63.75%)	⬇️
datadog_checks_base/tests/test_winpdh.py	40.81% <0.00%> (-57.15%)	⬇️
datadog_checks_base/tests/test_wmisampler.py	43.93% <0.00%> (-56.07%)	⬇️
datadog_checks_base/tests/test_wmicheck.py	69.23% <0.00%> (-30.77%)	⬇️
mapreduce/tests/common.py	91.35% <0.00%> (-2.47%)	⬇️
datadog_checks_base/datadog_checks/base/ddyaml.py	59.70% <0.00%> (-1.50%)	⬇️
etcd/tests/test_integration.py	97.43% <0.00%> (-0.86%)	⬇️
prometheus/datadog_checks/prometheus/prometheus.py
hazelcast/datadog_checks/hazelcast/__init__.py
... and 208 more