To solve this optimization problem using Gurobi, we first need to understand the variables and constraints provided. The objective function aims to minimize a weighted sum of milligrams of vitamin D, potassium, and vitamin K.

The variables are:
- `x0`: milligrams of vitamin D
- `x1`: milligrams of potassium
- `x2`: milligrams of vitamin K

Given the complexity and detail of the constraints, we will break down the problem formulation into steps to ensure clarity. However, due to the nature of your request for a direct translation into Gurobi code without intermediary explanations or code snippets outside the final block, I'll directly formulate the solution in Python using the Gurobi library.

Here is the code that captures the described optimization problem:

```python
from gurobipy import *

# Create a new model
model = Model("Optimization_Problem")

# Define variables (assuming non-negative)
x0 = model.addVar(vtype=GRB.INTEGER, name="milligrams_of_vitamin_D")
x1 = model.addVar(vtype=GRB.CONTINUOUS, name="milligrams_of_potassium")
x2 = model.addVar(vtype=GRB.INTEGER, name="milligrams_of_vitamin_K")

# Objective function
model.setObjective(8*x0 + 3*x1 + x2, GRB.MINIMIZE)

# Constraints based on the problem description
model.addConstr(x0 >= 0)
model.addConstr(x1 >= 0)
model.addConstr(x2 >= 0)

# Digestive support index constraints
model.addConstr(3*x0 + 3*x1 + 1*x2 >= 7, name="digestive_support_index_total")

# Cardiovascular support index constraints
model.addConstr(3*x0 + 5*x1 + 5*x2 >= 9, name="cardiovascular_support_index_total")
model.addConstr(3*x0 + 5*x2 >= 9, name="cardiovascular_support_vitamin_D_and_K")
model.addConstr(5*x1 + 5*x2 >= 6, name="cardiovascular_support_potassium_and_vitamin_K")

# Cognitive performance index constraints
model.addConstr(3*x0 + 2*x1 + 5*x2 >= 6, name="cognitive_performance_index_total")
model.addConstr(3*x0 + 5*x2 >= 3, name="cognitive_performance_vitamin_D_and_vitamin_K")
model.addConstr(2*x1 + 5*x2 >= 4, name="cognitive_performance_potassium_and_vitamin_K")

# Immune support index constraints
model.addConstr(x0 + x1 + 4*x2 >= 5, name="immune_support_index_total")
model.addConstr(x1 + 4*x2 >= 2, name="immune_support_potassium_and_vitamin_K")
model.addConstr(x0 + 4*x2 >= 6, name="immune_support_vitamin_D_and_vitamin_K")

# Energy stability index constraints
model.addConstr(x0 + 5*x1 + 2*x2 >= 8, name="energy_stability_index_total")
model.addConstr(5*x1 + 2*x2 >= 4, name="energy_stability_potassium_and_vitamin_K")
model.addConstr(x0 + 5*x1 >= 2, name="energy_stability_vitamin_D_and_potassium")

# Additional constraints
model.addConstr(-7*x0 + 8*x1 >= 0, name="additional_constraint_1")
model.addConstr(-7*x0 + 2*x2 >= 0, name="additional_constraint_2")

# Upper bounds for the total combined indices (if applicable)
model.addConstr(3*x0 + 2*x1 + 5*x2 <= 14, name="cognitive_performance_upper_bound")
model.addConstr(x0 + x1 + 4*x2 <= 10, name="immune_support_upper_bound")
model.addConstr(x0 + 2*x2 <= 15, name="energy_stability_vitamin_D_and_vitamin_K_upper_bound")
model.addConstr(5*x1 + 2*x2 <= 13, name="energy_stability_potassium_and_vitamin_K_upper_bound")

# Optimize the model
model.optimize()

# Print results
if model.status == GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Milligrams of Vitamin D: {x0.X}")
    print(f"Milligrams of Potassium: {x1.X}")
    print(f"Milligrams of Vitamin K: {x2.X}")
else:
    print("No optimal solution found.")
```