With the rapid development of plug-in electric vehicles （PEVs） and hydrogen fuel vehicles （HFVs）, introducing hybrid energy supply stations （HESSs） into microgrids is an important way to realize a green and low-carbon energy system. A HESS is a new type of energy infrastructure that can not only charge PEVs, but also refill HFVs. Currently, there is a lack of in-depth research on energy scheduling for microgrids with HESSs. Thus, this paper focuses on the distributed energy scheduling problem of multi-microgrids and virtual power plants with HESSs under the scenario of massive heterogeneous device integration. Firstly, a refined model of the HESS is formulated, and a cloud-edge collaboration based two-layer distributed energy scheduling framework is proposed to achieve optimal scheduling of heterogeneous devices such as electric vehicles, energy storage, and renewable energy sources. The upper and lower layers of the framework respectively consider the optimal scheduling of energy for a virtual power plant and multi-microgrids. Then, an analytical target cascading （ATC）-based distributed optimization algorithm is employed to decouple the upper- and lower-layer problems, thereby achieving the decentralized autonomy of the virtual power plant and microgrids. Finally, the effectiveness of the proposed cloud-edge collaborative method in achieving distributed optimization of the virtual power plant and microgrids is verified through case studies.