## Step 1: Define the symbolic representation of the variables
The variables are 'grams of protein', 'milligrams of magnesium', and 'milligrams of vitamin B7'. Let's denote them as $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Convert the objective function into a symbolic representation
The objective function to maximize is $6.34x_1^2 + 5.25x_1x_2 + 6.91x_1x_3 + 2.93x_2^2 + 1.22x_2x_3 + 8.63x_3^2 + 2.74x_1 + 9.5x_2 + 6.94x_3$.

## 3: Define the constraints in symbolic notation
The constraints are:
1. $13x_1 \leq 391$ (digestive support index for grams of protein, but this seems to be an upper bound on $x_1$ itself given $r0$),
2. $30x_2 \leq 391$,
3. $21x_3 \leq 391$,
4. $x_2^2 + x_3^2 \leq 131$,
5. $13x_1 + 30x_2 \leq 148$,
6. $13x_1 + 30x_2 + 21x_3 \leq 148$.

## 4: Express the problem in a suitable format for Gurobi
Given the objective function and constraints, we can now express the problem in a format suitable for Gurobi.

## 5: Write the Gurobi code
```python
import gurobi

# Create a new model
model = gurobi.Model()

# Define the variables
x1 = model.addVar(lb=0, name="grams_of_protein", vtype=gurobi.GRB.CONTINUOUS)
x2 = model.addVar(lb=0, name="milligrams_of_magnesium", vtype=gurobi.GRB.CONTINUOUS)
x3 = model.addVar(lb=0, name="milligrams_of_vitamin_B7", vtype=gurobi.GRB.CONTINUOUS)

# Objective function
model.setObjective(6.34*x1**2 + 5.25*x1*x2 + 6.91*x1*x3 + 2.93*x2**2 + 1.22*x2*x3 + 8.63*x3**2 + 2.74*x1 + 9.5*x2 + 6.94*x3, gurobi.GRB.MAXIMIZE)

# Constraints
model.addConstr(x1 <= 391/13, name="protein_digestive_support")
model.addConstr(x2 <= 391/30, name="magnesium_digestive_support")
model.addConstr(x3 <= 391/21, name="vitamin_B7_digestive_support")
model.addConstr(x2**2 + x3**2 <= 131, name="combined_magnesium_vitamin_B7")
model.addConstr(13*x1 + 30*x2 <= 148, name="combined_protein_magnesium")
model.addConstr(13*x1 + 30*x2 + 21*x3 <= 148, name="combined_all")

# 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 magnesium: {x2.varValue}")
    print(f"Milligrams of vitamin B7: {x3.varValue}")
    print(f"Objective function value: {model.objVal}")
else:
    print("No optimal solution found.")
```

## 6: Symbolic representation of the problem
```json
{
    'sym_variables': [('x1', 'grams of protein'), ('x2', 'milligrams of magnesium'), ('x3', 'milligrams of vitamin B7')],
    'objective_function': '6.34*x1^2 + 5.25*x1*x2 + 6.91*x1*x3 + 2.93*x2^2 + 1.22*x2*x3 + 8.63*x3^2 + 2.74*x1 + 9.5*x2 + 6.94*x3',
    'constraints': [
        'x1 <= 391/13',
        'x2 <= 391/30',
        'x3 <= 391/21',
        'x2^2 + x3^2 <= 131',
        '13*x1 + 30*x2 <= 148',
        '13*x1 + 30*x2 + 21*x3 <= 148'
    ]
}
```