To solve the optimization problem described, we first need to convert the natural language description into a symbolic representation. This involves defining variables for the quantities of interest (grams of protein and milligrams of vitamin D), formulating the objective function in terms of these variables, and translating the constraints into mathematical expressions.

Let's define:
- \(x_1\) as the amount of grams of protein,
- \(x_2\) as the amount of milligrams of vitamin D.

The objective function is to minimize: \(7x_1 + 3x_2\).

The constraints based on the description are:
1. Kidney support index from grams of protein: \(15x_1\)
2. Cardiovascular support index from grams of protein: \(5x_1\)
3. Kidney support index from milligrams of vitamin D: \(14x_2\)
4. Cardiovascular support index from milligrams of vitamin D: \(25x_2\)
5. Total combined kidney support index must be at least 33: \(15x_1 + 14x_2 \geq 33\)
6. Same as constraint 5, emphasizing the combination.
7. Total combined cardiovascular support index must be at least 78: \(5x_1 + 25x_2 \geq 78\)
8. Same as constraint 7, emphasizing the combination.
9. Constraint on grams of protein and milligrams of vitamin D: \(9x_1 - 6x_2 \geq 0\)
10. Total combined kidney support index should be 75 or less: \(15x_1 + 14x_2 \leq 75\)
11. Total combined cardiovascular support index must be as much or less than 189: \(5x_1 + 25x_2 \leq 189\)
12. Grams of protein must be an integer: \(x_1 \in \mathbb{Z}\)

Symbolic representation:
```json
{
    'sym_variables': [('x1', 'grams of protein'), ('x2', 'milligrams of vitamin D')],
    'objective_function': '7*x1 + 3*x2',
    'constraints': [
        '15*x1 + 14*x2 >= 33',
        '5*x1 + 25*x2 >= 78',
        '9*x1 - 6*x2 >= 0',
        '15*x1 + 14*x2 <= 75',
        '5*x1 + 25*x2 <= 189'
    ]
}
```

To implement this in Gurobi, we need to use Python. The reasoning behind the code is to directly translate the symbolic representation into Gurobi's modeling language.

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(vtype=GRB.INTEGER, name="grams_of_protein")
x2 = m.addVar(vtype=GRB.CONTINUOUS, name="milligrams_of_vitamin_D")

# Set objective function
m.setObjective(7*x1 + 3*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(15*x1 + 14*x2 >= 33, name="kidney_support_index_min")
m.addConstr(5*x1 + 25*x2 >= 78, name="cardiovascular_support_index_min")
m.addConstr(9*x1 - 6*x2 >= 0, name="protein_vitamin_constraint")
m.addConstr(15*x1 + 14*x2 <= 75, name="kidney_support_index_max")
m.addConstr(5*x1 + 25*x2 <= 189, name="cardiovascular_support_index_max")

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Grams of Protein: {x1.x}")
    print(f"Milligrams of Vitamin D: {x2.x}")
    print(f"Objective Function Value: {m.objVal}")
else:
    print("No optimal solution found")
```