This paper presents a novel context-sensitive multi\-agent coordination for dynamic resource allocation (CAMAC-DRA) framework for optimizing smart electric vehicle (EV) charging ecosystems through the Smart2Charge application. The proposed system coordinates autonomous charging agents across networks of 250 EVs and 45 charging stations while adapting to dynamic environmental conditions through context-aware decision-making. Our multi-agent approach employs coordinated Deep Q\-Networks integrated with Graph Neural Networks and attention mechanisms, processing 20 contextual features including weather patterns, traffic conditions, grid load fluctuations, and electricity pricing.The framework balances five ecosystem stakeholders i.e. EV users (25\%), grid operators (20\%), charging station operators (20\%), fleet operators (20%), and environmental factors (15\%) through weighted coordination mechanisms and consensus protocols. Comprehensive validation using real-world datasets containing 441,077 charging transactions demonstrates superior performance compared to baseline algorithms including DDPG, A3C, PPO, and GNN approaches. The CAMAC\-DRA framework achieves 92\% coordination success rate, 15\% energy efficiency improvement, 10\% cost reduction, 20% grid strain decrease, and \2.3x faster convergence while maintaining 88\% training stability and 85\% sample efficiency. Real-world validation confirms commercial viability with Net Present Cost of -\$122,962 and 69\% cost reduction through renewable energy integration. The framework's unique contribution lies in developing context-aware multi-stakeholder coordination that successfully balances competing objectives while adapting to real-time variables, positioning it as a breakthrough solution for intelligent EV charging coordination and sustainable transportation electrification.