Open RAN (O-RAN) fosters multi-vendor interoperability and data-driven control but simultaneously introduces the challenge of coordinating pre-trained xApps that may produce conflicting actions. Although O-RAN specifications mandate offline training and validation to prevent the deployment of untrained or inadequately tested models, operational conflicts can still arise under dynamic and context-dependent conditions.This work proposes a scheduler-based conflict mitigation framework to address these challenges without requiring training xApps together or further xApp re-training. By examining an indirect conflict involving power and resource block allocation xApps and employing an Advantage Actor-Critic (A2C) approach to train both xApps and the scheduler, we illustrate that a straightforward A2C-based scheduler improves performance relative to independently deployed xApps and conflicting cases. Notably, among all tested deployment scenarios (including individual xApp deployment, multiple conflicting xApps, and limited scheduler configurations), augmenting the system with baseline xApps and enabling the scheduler to select from a broader pool achieves the highest total transmission rate, thereby underscoring the importance of adaptive scheduling mechanisms. These findings highlight the context-dependent nature of conflicts in automated network management, as two xApps may conflict under certain conditions but coexist under others. Consequently, the ability to dynamically update and adapt the scheduler to accommodate diverse operational intents is vital for future network deployments. By offering dynamic scheduling without re-training xApps, this framework advances practical conflict resolution solutions while supporting real-world scalability.