In heterogeneous scientific teams, proactive team agents can serve as effective assistants regarding the research progress of the project. However, proactive agents always suffer from collaborative myopia: a greedy optimization for immediate task accuracy which ignore the long-term goal of team sustainability. This leads to the Individual-centric Trap, where capable experts (e.g., PIs) are disproportionately overloaded while Junior roles remain underutilized. Therefore, neglecting opportunity costs in task allocation can implicitly erodes the enduring performance of the team. To solve this imbalance between efficiency and sustainability, we propose GT-PMARL (Game-Theoretic Proactive Multi-Agent Reinforcement Learning). By internalizing the opportunity cost as a key consideration in individual decision-making, the collaboration logic of agents has been reshaped. Our framework employs: (1) a Positive-Unlabeled scorer to anchor intervention quality under sparse supervision; (2) a Nash-Pareto competitive objective to seek an equilibrium between individual task excellence and collective load balancing. Empirical experiments in scientific workflows show that GT-PMARL effectively maintains high performance while preventing experts from over-developing. Our work provides a scalable paradigm for building a sustainable and balanced human-AI collaborative ecosystem.