[Feat]: Telemetry to collect anonymous A2A usage across all SDK

### Is your feature request related to a problem? Please describe.

It is nearly impossible to measure adoption of A2A.

Github stars and SDK downloads are the only metrics we have.

**Ideal metrics:**

- Monthly Active Agents using A2A (have had any iteractions)
- Monthly sum of A2A tasks

Metric dimensions (MECE):

- Breakdown by A2A task terminal status
- Breakdown by client/server
- Breakdown by SDK language and version number
- Breakdown by data binding or transport

Optional Metric dimensions (MECE):

- Breakdown by company names (domains)
- Breakdown by agent card attributes

### Describe the solution you'd like

**Recommendation:**

https://about.scarf.sh/post/the-open-source-business-metrics-guide

NOTE: I would be fine with any solution which solved the aggregate anonymous metrics needs, this is what https://copilotkit.ai uses and seems sufficient.

**Concerns:**

- transparency (default on but opt-out)
- privacy (must not accidentally collect sensitive data)
- latency and performance (must not slow down execution)

Other open source projects have adopted 3rd party telemetry providers, and I am only recommending we adopt a path which does pass legal review as described in this policy:

https://www.linuxfoundation.org/legal/telemetry-data-policy

### Describe alternatives you've considered

- Google Analytics (too much data collection)
- no metrics (not helpful)

### Additional context

_No response_

### Code of Conduct

- [x] I agree to follow this project's Code of Conduct

The issue is about adding telemetry across all SDKs to collect anonymous usage metrics. Start by reviewing the linked Scarf.sh guide and the Linux Foundation telemetry data policy. Examine the existing SDK codebase to understand how agents are instrumented and where telemetry calls could be added without performance impact. Determine what 'done' looks like by defining the specific metrics to collect and ensuring they are anonymized and opt-out by default.

Escrito por el modelo de indexación a partir del texto del issue.