To solve the optimization problem described, we first need to translate the natural language description into a symbolic representation. This involves defining variables for each of the items mentioned (grams of protein, milligrams of vitamin B12, and milligrams of vitamin C), formulating the objective function using these variables, and then listing all constraints in terms of these variables.

Let's denote:
- \(x_1\) as the grams of protein,
- \(x_2\) as the milligrams of vitamin B12,
- \(x_3\) as the milligrams of vitamin C.

The objective function to be maximized is given by:
\[5x_1^2 + 3x_2x_3 + 9x_3^2 + 5x_1 + 7x_3\]

The constraints are:
1. The kidney support index for grams of protein is 2, which implies \(2x_1\).
2. Milligrams of vitamin B12 have a kidney support index of 14, implying \(14x_2\).
3. Milligrams of vitamin C have a kidney support index of 27, so \(27x_3\).
4. The total combined kidney support index from all three must be greater than or equal to 85: \(2x_1 + 14x_2 + 27x_3 \geq 85\).
5. The total combined kidney support index from grams of protein and milligrams of vitamin B12 should be as much or less than 227: \(2x_1 + 14x_2 \leq 227\).
6. The total combined kidney support index from grams of protein and milligrams of vitamin C should be at most 117: \(2x_1 + 27x_3 \leq 117\).
7. The total combined kidney support index from all three should be at most 326: \(2x_1 + 14x_2 + 27x_3 \leq 326\).

Given the nature of the problem, we are dealing with a nonlinear optimization problem due to the terms in the objective function and possibly in the constraints. However, Gurobi can handle quadratic terms directly.

```json
{
    'sym_variables': [('x1', 'grams of protein'), ('x2', 'milligrams of vitamin B12'), ('x3', 'milligrams of vitamin C')],
    'objective_function': '5*x1**2 + 3*x2*x3 + 9*x3**2 + 5*x1 + 7*x3',
    'constraints': [
        '2*x1 + 14*x2 + 27*x3 >= 85',
        '2*x1 + 14*x2 <= 227',
        '2*x1 + 27*x3 <= 117',
        '2*x1 + 14*x2 + 27*x3 <= 326'
    ]
}
```

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

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(vtype=GRB.CONTINUOUS, name="grams_of_protein")
x2 = m.addVar(vtype=GRB.CONTINUOUS, name="milligrams_of_vitamin_B12")
x3 = m.addVar(vtype=GRB.CONTINUOUS, name="milligrams_of_vitamin_C")

# Define the objective function
m.setObjective(5*x1**2 + 3*x2*x3 + 9*x3**2 + 5*x1 + 7*x3, GRB.MAXIMIZE)

# Add constraints
m.addConstr(2*x1 + 14*x2 + 27*x3 >= 85, "kidney_support_index_all")
m.addConstr(2*x1 + 14*x2 <= 227, "kidney_support_index_protein_B12")
m.addConstr(2*x1 + 27*x3 <= 117, "kidney_support_index_protein_C")
m.addConstr(2*x1 + 14*x2 + 27*x3 <= 326, "total_kidney_support_index")

# 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 B12: {x2.x}")
    print(f"Milligrams of Vitamin C: {x3.x}")
else:
    print("No optimal solution found")
```