## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'milligrams of vitamin A' and 'milligrams of vitamin B9'. Let's denote 'milligrams of vitamin A' as $x_0$ and 'milligrams of vitamin B9' as $x_1$. The objective function to maximize is $4x_0 + 3x_1$.

## Step 2: List the constraints in symbolic notation
The constraints given are:
- Digestive support index for $x_0$ is 7, for $x_1$ is 14. The total must be 42 or more: $7x_0 + 14x_1 \geq 42$
- Cardiovascular support index for $x_0$ is 16, for $x_1$ is 7. The total must be at least 29: $16x_0 + 7x_1 \geq 29$
- Immune support index for $x_0$ is 15, for $x_1$ is 6. The total must be 69 at minimum: $15x_0 + 6x_1 \geq 69$
- Kidney support index for $x_0$ is 9, for $x_1$ is 17. The total must be 43 or more: $9x_0 + 17x_1 \geq 43$
- $5x_0 - 3x_1 \geq 0$
- Total digestive support index must be 132 at a maximum: $7x_0 + 14x_1 \leq 132$
- Total cardiovascular support index must be 94 at a maximum: $16x_0 + 7x_1 \leq 94$
- Total immune support index must be 129 or less: $15x_0 + 6x_1 \leq 129$
- Total kidney support index must be 113 at a maximum: $9x_0 + 17x_1 \leq 113$

## 3: Provide the symbolic representation of the problem
The symbolic representation is:
```json
{
'sym_variables': [('x0', 'milligrams of vitamin A'), ('x1', 'milligrams of vitamin B9')],
'objective_function': '4*x0 + 3*x1',
'constraints': [
    '7*x0 + 14*x1 >= 42',
    '16*x0 + 7*x1 >= 29',
    '15*x0 + 6*x1 >= 69',
    '9*x0 + 17*x1 >= 43',
    '5*x0 - 3*x1 >= 0',
    '7*x0 + 14*x1 <= 132',
    '16*x0 + 7*x1 <= 94',
    '15*x0 + 6*x1 <= 129',
    '9*x0 + 17*x1 <= 113'
]
}
```

## 4: Write the Gurobi code for the optimization problem
```python
import gurobi

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

    # Define the variables
    x0 = model.addVar(name="x0", lb=0)  # milligrams of vitamin A
    x1 = model.addVar(name="x1", lb=0)  # milligrams of vitamin B9

    # Define the objective function
    model.setObjective(4 * x0 + 3 * x1, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(7 * x0 + 14 * x1 >= 42, name="digestive_support_index_min")
    model.addConstr(16 * x0 + 7 * x1 >= 29, name="cardiovascular_support_index_min")
    model.addConstr(15 * x0 + 6 * x1 >= 69, name="immune_support_index_min")
    model.addConstr(9 * x0 + 17 * x1 >= 43, name="kidney_support_index_min")
    model.addConstr(5 * x0 - 3 * x1 >= 0, name="vitamin_constraint")
    model.addConstr(7 * x0 + 14 * x1 <= 132, name="digestive_support_index_max")
    model.addConstr(16 * x0 + 7 * x1 <= 94, name="cardiovascular_support_index_max")
    model.addConstr(15 * x0 + 6 * x1 <= 129, name="immune_support_index_max")
    model.addConstr(9 * x0 + 17 * x1 <= 113, name="kidney_support_index_max")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of vitamin A: {x0.varValue}")
        print(f"Milligrams of vitamin B9: {x1.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```