The rapid expansion of computational science has transformed software, workflows, and digital artifacts into primary vehicles of scientific knowledge production. As research becomes increasingly data intensive and collaborative, the need for interoperable, reproducible, and sustainable software ecosystems has emerged as a central challenge. This article develops a comprehensive theoretical framework that integrates semantic interoperability, workflow standardization, provenance modeling, software citation, and FAIR data and software principles into a unified model for sustainable scientific software ecosystems. Drawing exclusively on established literature concerning semantic interoperability, enterprise interoperability assessment, workflow languages, provenance ontologies, FAIR principles, and software sustainability initiatives, this work examines how technical, organizational, and semantic dimensions of interoperability converge within modern research infrastructures. Particular attention is given to scientific workflow systems, executable notebooks, service oriented architectures, and metadata schemas as foundational mechanisms that enable reuse, replication, and long term preservation. The analysis further investigates interoperability assessment models, service composition strategies, and the role of community driven standards bodies in fostering trust and transparency. By synthesizing perspectives from semantic web research, enterprise systems engineering, reproducible research scholarship, and software citation theory, the article proposes a multidimensional interoperability maturity framework tailored to scientific software. The findings suggest that interoperability cannot be reduced to syntactic compatibility alone but must incorporate semantic alignment, governance structures, provenance capture, and formal citation practices. Moreover, the application of FAIR principles to software artifacts is shown to require adaptation beyond data centric interpretations, particularly in relation to executability and sustainability. The discussion elaborates on theoretical tensions between flexibility and standardization, automation and transparency, and innovation and preservation. Limitations and future research directions are articulated in relation to assessment metrics, policy harmonization, and cross disciplinary generalization. This work contributes to the emerging scholarship on digital research infrastructures by offering a unified conceptual model that aligns workflow interoperability, FAIRness, provenance ontologies, and sustainable software governance into a coherent foundation for reproducible and reusable science.