## Step 1: Define the symbolic representation of the problem
The problem involves maximizing an objective function subject to several constraints. Let's define the symbolic variables:
- $x_1$ represents 'grams of protein'
- $x_2$ represents 'milligrams of vitamin B12'
- $x_3$ represents 'milligrams of vitamin C'

The objective function to maximize is: $5x_1^2 + 3x_2x_3 + 9x_3^2 + 5x_1 + 7x_3$

## Step 2: Translate the constraints into symbolic notation
The constraints given are:
- The kidney support index for grams of protein is 2: $2x_1$
- The kidney support index for milligrams of vitamin B12 is 14: $14x_2$
- The kidney support index for milligrams of vitamin C is 27: $27x_3$
- The total combined kidney support index must be greater than or equal to 85: $2x_1 + 14x_2 + 27x_3 \geq 85$
- The total combined kidney support index from grams of protein and milligrams of vitamin B12 should be as much or less than 227: $2x_1 + 14x_2 \leq 227$
- The total combined kidney support index from grams of protein and milligrams of vitamin C should be at most 117: $2x_1 + 27x_3 \leq 117$
- The total combined kidney support index from all three should be at most 326: $2x_1 + 14x_2 + 27x_3 \leq 326$

## 3: Provide the symbolic representation of the problem
```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'
    ]
}
```

## Step 4: Implement the optimization problem using Gurobi
```python
import gurobi

def solve_optimization_problem():
    # Create a new model
    model = gurobi.Model()

    # Define the variables
    x1 = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="grams_of_protein")
    x2 = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="milligrams_of_vitamin_B12")
    x3 = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="milligrams_of_vitamin_C")

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

    # Add constraints
    model.addConstr(2*x1 + 14*x2 + 27*x3 >= 85, name="combined_kidney_support_index")
    model.addConstr(2*x1 + 14*x2 <= 227, name="protein_B12_kidney_support")
    model.addConstr(2*x1 + 27*x3 <= 117, name="protein_C_kidney_support")
    model.addConstr(2*x1 + 14*x2 + 27*x3 <= 326, name="total_kidney_support")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Grams of protein: {x1.varValue}")
        print(f"Milligrams of vitamin B12: {x2.varValue}")
        print(f"Milligrams of vitamin C: {x3.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```