## Step 1: Define the symbolic representation of the variables
The variables are 'milligrams of vitamin B2', 'milligrams of magnesium', and 'milligrams of zinc'. Let's denote them as $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Convert the natural language description into a symbolic representation
The symbolic representation of the variables is:
- $x_1$ = milligrams of vitamin B2
- $x_2$ = milligrams of magnesium
- $x_3$ = milligrams of zinc

## 3: Define the objective function in symbolic notation
The objective function to maximize is $6.31x_1 + 9.68x_2 + 1.01x_3$.

## 4: List the constraints in symbolic notation
The constraints are:
- $3x_1 + x_2 + 24x_3 \geq 23$ (kidney support index from $x_1$ and $x_3$)
- $3x_1 + x_2 \geq 34$ (kidney support index from $x_1$ and $x_2$)
- $x_2 + 24x_3 \geq 23$ (kidney support index from $x_2$ and $x_3$)
- $2x_1 + 19x_2 \leq 306$ (digestive support index from $x_1$ and $x_2$)
- $2x_1 + 6x_3 \leq 213$ (digestive support index from $x_1$ and $x_3$)
- $2x_1 + 19x_2 + 6x_3 \leq 213$ (digestive support index from $x_1$, $x_2$, and $x_3$)
- $3x_1 + 24x_3 \leq 154$ (kidney support index from $x_1$ and $x_3$)
- $x_2 + 24x_3 \leq 139$ (kidney support index from $x_2$ and $x_3$)
- $3x_1 + x_2 + 24x_3 \leq 65$ (kidney support index from $x_1$, $x_2$, and $x_3$)

## 5: Provide the symbolic representation in JSON format
```json
{
    'sym_variables': [('x1', 'milligrams of vitamin B2'), ('x2', 'milligrams of magnesium'), ('x3', 'milligrams of zinc')],
    'objective_function': '6.31*x1 + 9.68*x2 + 1.01*x3',
    'constraints': [
        '3*x1 + x2 + 24*x3 >= 23',
        '3*x1 + x2 >= 34',
        'x2 + 24*x3 >= 23',
        '2*x1 + 19*x2 <= 306',
        '2*x1 + 6*x3 <= 213',
        '2*x1 + 19*x2 + 6*x3 <= 213',
        '3*x1 + 24*x3 <= 154',
        'x2 + 24*x3 <= 139',
        '3*x1 + x2 + 24*x3 <= 65'
    ]
}
```

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

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

    # Define the variables
    x1 = model.addVar(name="x1", lb=0)  # milligrams of vitamin B2
    x2 = model.addVar(name="x2", lb=0)  # milligrams of magnesium
    x3 = model.addVar(name="x3", lb=0)  # milligrams of zinc

    # Define the objective function
    model.setObjective(6.31 * x1 + 9.68 * x2 + 1.01 * x3, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(3 * x1 + x2 + 24 * x3 >= 23)  
    model.addConstr(3 * x1 + x2 >= 34)  
    model.addConstr(x2 + 24 * x3 >= 23)  
    model.addConstr(2 * x1 + 19 * x2 <= 306)  
    model.addConstr(2 * x1 + 6 * x3 <= 213)  
    model.addConstr(2 * x1 + 19 * x2 + 6 * x3 <= 213)  
    model.addConstr(3 * x1 + 24 * x3 <= 154)  
    model.addConstr(x2 + 24 * x3 <= 139)  
    model.addConstr(3 * x1 + x2 + 24 * x3 <= 65)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of vitamin B2: {x1.varValue}")
        print(f"Milligrams of magnesium: {x2.varValue}")
        print(f"Milligrams of zinc: {x3.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

optimize_problem()
```