Switch from black and flake8 to ruff for Python files

[FlorentClarret]

What does this PR do?

We currently use:

• flake8: linter
• black: code formatter
• isort: sort the imports

We drop them all to replace them with ruff which can do the same things! I also updated the hooks, people that are using them won't need to do anything.

I kept almost the same rules we already have to avoid modifying too many things. We will probably update some of them in the future.

Motivation

• The versions that we use are getting old and we will need to bump them eventually (more than 3 years for flake8).
• Ruff can now format the code, so that will be less tools to use, install and maintain.
• Even if we do not have a lot of python files, ruff is also faster than the tools we use.
• This is the recommended tool to use in our python community
• Ruff is faster than them, but this is not that important here because we do not have a lot of python files
• We also use this tool in integrations-core

Additional Notes

• Most Python files in this repo are either tests or tools. This is not going to impact the agent itself.
• I'll drop the dependencies from the base image in a follow-up PR
• We won't merge this PR right away and make several announcements first so people will be warned
• People that are using the invoke task will need to pip install the dependencies

This PR depends on:

• Add ruff to the Python deps datadog-agent-buildimages#568
• Add ruff to the requirements file datadog-agent-buildimages#569
• Bump the buildimages tags #24665

https://datadoghq.atlassian.net/browse/ADXT-186

Possible Drawbacks / Trade-offs

Describe how to test/QA your changes

[pr-commenter]

Regression Detector

Regression Detector Results

Run ID: 7c3a6e11-25f9-49f6-9144-1a6ef15c1d47
Baseline: ffb7c2f
Comparison: a8fcc26

Performance changes are noted in the perf column of each table:

• ✅ = significantly better comparison variant performance
• ❌ = significantly worse comparison variant performance
• ➖ = no significant change in performance

No significant changes in experiment optimization goals

Confidence level: 90.00%
Effect size tolerance: |Δ mean %| ≥ 5.00%

There were no significant changes in experiment optimization goals at this confidence level and effect size tolerance.

Experiments ignored for regressions

Regressions in experiments with settings containing erratic: true are ignored.

perf	experiment	goal	Δ mean %	Δ mean % CI
➖	file_to_blackhole	% cpu utilization	+2.34	[-3.71, +8.40]

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI
➖	file_to_blackhole	% cpu utilization	+2.34	[-3.71, +8.40]
➖	pycheck_1000_100byte_tags	% cpu utilization	+2.22	[-2.72, +7.16]
➖	process_agent_standard_check_with_stats	memory utilization	+0.35	[+0.30, +0.39]
➖	otel_to_otel_logs	ingress throughput	+0.27	[-0.14, +0.69]
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	+0.14	[-2.81, +3.09]
➖	process_agent_standard_check	memory utilization	+0.11	[+0.06, +0.17]
➖	file_tree	memory utilization	+0.03	[-0.09, +0.15]
➖	trace_agent_msgpack	ingress throughput	+0.02	[+0.01, +0.03]
➖	uds_dogstatsd_to_api	ingress throughput	+0.01	[-0.19, +0.22]
➖	tcp_dd_logs_filter_exclude	ingress throughput	-0.01	[-0.04, +0.03]
➖	trace_agent_json	ingress throughput	-0.01	[-0.03, +0.01]
➖	idle	memory utilization	-0.10	[-0.14, -0.06]
➖	process_agent_real_time_mode	memory utilization	-0.12	[-0.17, -0.08]
➖	tcp_syslog_to_blackhole	ingress throughput	-0.14	[-0.23, -0.06]
➖	basic_py_check	% cpu utilization	-1.64	[-4.15, +0.86]

Explanation

A regression test is an A/B test of target performance in a repeatable rig, where "performance" is measured as "comparison variant minus baseline variant" for an optimization goal (e.g., ingress throughput). Due to intrinsic variability in measuring that goal, we can only estimate its mean value for each experiment; we report uncertainty in that value as a 90.00% confidence interval denoted "Δ mean % CI".

For each experiment, we decide whether a change in performance is a "regression" -- a change worth investigating further -- if all of the following criteria are true:

1. Its estimated |Δ mean %| ≥ 5.00%, indicating the change is big enough to merit a closer look.

2. Its 90.00% confidence interval "Δ mean % CI" does not contain zero, indicating that if our statistical model is accurate, there is at least a 90.00% chance there is a difference in performance between baseline and comparison variants.

3. Its configuration does not mark it "erratic".

[louis-cqrl]

LGTM for files that ASC owns

[FlorentClarret]

/merge

[dd-devflow]

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