To solve this optimization problem, we need to first identify the decision variables and the objective function. The decision variables are 'milligrams of vitamin C' (x0) and 'milligrams of vitamin B7' (x1). The objective function is to maximize 3.56*x0 + 9.33*x1.

We also have several constraints based on the cardiovascular support index, digestive support index, kidney support index, and energy stability index for both vitamins. Additionally, we have constraints that involve linear combinations of x0 and x1.

Here's a breakdown of how these constraints can be translated into mathematical expressions:

- Cardiovascular support index: 5.67*x0 + 6.06*x1 >= 84 (minimum) and <= 248 (maximum)
- Digestive support index: 0.73*x0 + 4.16*x1 >= 21 (minimum) and <= 90 (maximum)
- Kidney support index: 0.37*x0 + 1.26*x1 >= 48 (minimum) and <= 128 (maximum)
- Energy stability index: 3.56*x0 + 1.87*x1 >= 58 (minimum) and <= 127 (maximum)
- Additional constraint: 5*x0 - 3*x1 >= 0

Now, let's write the Gurobi code for this optimization problem:

```python
from gurobipy import *

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

# Define decision variables
x0 = m.addVar(lb=0, ub=GRB.INFINITY, vtype=GRB.CONTINUOUS, name="milligrams_of_vitamin_C")
x1 = m.addVar(lb=0, ub=GRB.INFINITY, vtype=GRB.CONTINUOUS, name="milligrams_of_vitamin_B7")

# Define the objective function
m.setObjective(3.56*x0 + 9.33*x1, GRB.MAXIMIZE)

# Add constraints
m.addConstr(5.67*x0 + 6.06*x1 >= 84, "cardiovascular_support_index_min")
m.addConstr(5.67*x0 + 6.06*x1 <= 248, "cardiovascular_support_index_max")
m.addConstr(0.73*x0 + 4.16*x1 >= 21, "digestive_support_index_min")
m.addConstr(0.73*x0 + 4.16*x1 <= 90, "digestive_support_index_max")
m.addConstr(0.37*x0 + 1.26*x1 >= 48, "kidney_support_index_min")
m.addConstr(0.37*x0 + 1.26*x1 <= 128, "kidney_support_index_max")
m.addConstr(3.56*x0 + 1.87*x1 >= 58, "energy_stability_index_min")
m.addConstr(3.56*x0 + 1.87*x1 <= 127, "energy_stability_index_max")
m.addConstr(5*x0 - 3*x1 >= 0, "additional_constraint")

# Optimize the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"milligrams_of_vitamin_C: {x0.x}")
    print(f"milligrams_of_vitamin_B7: {x1.x}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found.")
```