The joint classification of multisource remote sensing data is a prominent research field. However, most of the existing works are tailored for two specific data sources, which fail to effectively address the diverse combinations of data sources in practical applications. The importance of designing a unified network with applicability has been disregarded. In this paper, we propose a unified and self-supervised Symbiotic Diffusion framework (named SymDiffuser), which achieves the joint classification of any pair of different remote sensing data sources in a single model. The SymDiffuser captures the inter-modal relationship through establishing reciprocal conditional distributions across diverse sources step by step. The fusion process of multisource data is consistently represented within the framework from a data distribution perspective. Subsequently, features under the current conditional distribution at each time step is integrated during the downstream phase to accomplish the classification task. Such joint classification methodology transcends source-specific considerations, rendering it applicable to remote sensing data from any diverse sources. The experimental results showcase the framework's potential in achieving state-of-the-art performance in multimodal fusion classification task.