feat: Implement evalFeatures() for optimized batch evaluation

[vazarkevych]

Summary

In this PR we have Implemented batch feature evaluation API to optimize memory usage when evaluating multiple features for the same user.

Changes

• Added evalFeatures() method to GrowthBookClient for batch evaluation
• Added evalFeatures() method to GrowthBook for API consistency
• Extracted mergeAttributes() helper method to avoid code duplication

[shubham-300]

Memory Issue: EvaluationContext created per feature

The current implementation creates a new EvaluationContext inside the loop for every feature evaluation. Each EvaluationContext holds a reference to GlobalContext, which contains the entire parsed features map and saved groups (potentially hundreds of MBs). When evaluating thousands of features in parallel batches, this results in thousands of EvaluationContext copies being held in memory simultaneously (10GB+ observed), causing excessive memory pressure and GC overhead.

Root cause: EvaluationContext is created per feature instead of being reused across all features in the batch.

Solution: Create EvaluationContext once outside the loop and reuse it for all feature evaluations in the batch. Since we've already merged attributes once into optimizedUserContext, the same EvaluationContext can safely be reused for evaluating all features, reducing memory allocation from O(n * size(GlobalContext)) to O(size(GlobalContext)) per batch.

[vazarkevych]

Thanks for the review. I have moved EvaluationContext out of the loop and reuse it, but after each evaluation I set a new StackContext to prevent circular dependency.