core: incorrect token count (`usage_metadata`) in streaming mode

### Checked other resources - [x] I added a very descriptive title to this issue. - [x] I searched the LangChain documentation with the integrated search. - [x] I used the GitHub search to find a similar question and didn't find it. - [x] I am sure that this is a bug in LangChain rather than my code. - [x] The bug is not resolved by updating to the latest stable version of LangChain (or the specific integration package). ### Example Code Any LLM-call with streaming. The aggregated token usage is totally wrong and much to high. See this method: https://github.com/langchain-ai/langchain/blob/b75573e858a3b53427675f551e74dfd7e1dbb4c6/libs/core/langchain_core/messages/ai.py#L406 ``` # Token usage if left.usage_metadata or any(o.usage_metadata is not None for o in others): usage_metadata: Optional[UsageMetadata] = left.usage_metadata for other in others: usage_metadata = add_usage(usage_metadata, other.usage_metadata) else: usage_metadata = None ``` For streaming we get usage_metdata for each token, e.g. 'input_tokens' = 713 'output_tokens' = 1 'total_tokens' = 714 output_tokens is always 1 and adds up nicely. input_tokens is always 713 for llm-token-stream and adds up to "input_tokens" * "count(tokens)" (same total_tokens with 714) This just adds up tokens to huge (totally useless) numbers. What is the strategy here? Should the llm not report per-token usage metdata and only report this in final chunk? Then Langchain-openai has to change this for that call: https://github.com/langchain-ai/langchain/blob/b75573e858a3b53427675f551e74dfd7e1dbb4c6/libs/partners/openai/langchain_openai/chat_models/base.py#L2805 ### Error Message and Stack Trace (if applicable) _No response_ ### Description * I'm trying to get sane token usage numbers for streaming with usage_metadata * I get hugely inflated total_tokens and input_tokens (because multiplied by count(output_token) * Define a strategy and either adapt the token aggregation in lan…