Microgrids equipped with distributed renewable energy resources, energy storage systems, controllable loads, and vehicle-to-grid (V2G) technologies have emerged as critical enablers for sustainable energy transitions. However, their effective integration into day-ahead electricity markets requires advanced decentralized coordination mechanisms that ensure both robust scheduling under uncertainty and the preservation of agent privacy. Hence, this paper introduces a two-layer optimization framework to coordinate residential and industrial microgrids with the distribution network operator (DNO), addressing economic, technical, and environmental objectives. The proposed model employs an enhanced alternating direction method of multipliers (ADMM) algorithm, which dynamically adjusts power exchange prices based on carbon tax rates. This approach incentivizes low-carbon operations while preserving the confidentiality of internal microgrid schedules. Residential and industrial microgrids perform day-ahead scheduling in the first layer by leveraging the flexible capacities of their diverse technologies. Subsequently, they submit their desired exchange plans for participation in the day-ahead electricity market to the DNO in the second layer, where the feasibility of their implementation is evaluated. The model was validated on a 123-bus electricity distribution network comprising 32 residential microgrids and 17 industrial microgrids. The results demonstrated its effectiveness, achieving a 12.19 % reduction in carbon emissions and a 11.24 % decrease in operational costs. Furthermore, the proposed enhanced ADMM reduced convergence time by 43.16 % compared to the standard ADMM, significantly expediting coordination among decentralized agents in day-ahead markets.