fix: Fix incorrect token count mapping in telemetry

Merge #2109

Fixes #2105

## Problem
  When integrating Google ADK with Langfuse using the @observe
  decorator, the usage details displayed in Langfuse web UI were
  incorrect.

  The root cause was in the telemetry implementation where
  total_token_count was being mapped to gen_ai.usage.output_tokens
  instead of candidates_token_count.

  - Expected mapping:
    - candidates_token_count → completion_tokens (output tokens)
    - prompt_token_count → prompt_tokens (input tokens)

  - Previous incorrect mapping:
    - total_token_count → completion_tokens (wrong!)
    - prompt_token_count → prompt_tokens (correct)

  ## Solution
  Updated trace_call_llm function in telemetry.py to use
  candidates_token_count for output token tracking instead of
  total_token_count, ensuring proper token count reporting to
  observability tools like Langfuse.

  ## Testing plan
  - Updated test expectations in test_telemetry.py
  - Verified telemetry tests pass
  - Manual verification with Langfuse integration

  ## Screenshots
  **Before**

<img width="1187" height="329" alt="Screenshot from 2025-07-22 20-20-33" src="https://github.com/user-attachments/assets/ad5fc957-64a2-4524-bd31-0cebb15a5270" />

  **After**

<img width="1187" height="329" alt="Screenshot from 2025-07-22 20-21-40" src="https://github.com/user-attachments/assets/3920df2a-be75-47e0-9bd0-f961bb72c838" />

 _Notes_: From the screenshot, there's another problem: thoughts_token_count field is not mapped, but this should be another issue imo

COPYBARA_INTEGRATE_REVIEW=#2109 from tl-nguyen:fix-telemetry-token-count-mapping 3d043f5
PiperOrigin-RevId: 786827802

Author: tl-nguyen

src/google/adk/telemetry.py

@@ -202,7 +202,7 @@ def trace_call_llm(
     )
     span.set_attribute(
         'gen_ai.usage.output_tokens',
-        llm_response.usage_metadata.total_token_count,
+        llm_response.usage_metadata.candidates_token_count,
     )
 
 

tests/unittests/test_telemetry.py

@@ -155,15 +155,17 @@ async def test_trace_call_llm_usage_metadata(monkeypatch, mock_span_fixture):
   llm_response = LlmResponse(
       turn_complete=True,
       usage_metadata=types.GenerateContentResponseUsageMetadata(
-          total_token_count=100, prompt_token_count=50
+          total_token_count=100,
+          prompt_token_count=50,
+          candidates_token_count=50,
       ),
   )
   trace_call_llm(invocation_context, 'test_event_id', llm_request, llm_response)
 
   expected_calls = [
       mock.call('gen_ai.system', 'gcp.vertex.agent'),
       mock.call('gen_ai.usage.input_tokens', 50),
-      mock.call('gen_ai.usage.output_tokens', 100),
+      mock.call('gen_ai.usage.output_tokens', 50),
   ]
   assert mock_span_fixture.set_attribute.call_count == 9
   mock_span_fixture.set_attribute.assert_has_calls(