## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'milligrams of vitamin B12' and 'milligrams of vitamin B7', which can be represented symbolically as $x_1$ and $x_2$ respectively.

## Step 2: Translate the objective function into symbolic notation
The objective function to maximize is $9.14x_1 + 5.53x_2$.

## 3: Define the constraints in symbolic notation
The constraints given are:
- The cardiovascular support index for $x_1$ is 11.
- The energy stability index for $x_1$ is 12.
- The cardiovascular support index for $x_2$ is 10.
- The energy stability index for $x_2$ is 3.
- The total combined cardiovascular support index is at least 23: $11x_1 + 10x_2 \geq 23$.
- The total combined energy stability index is at least 16: $12x_1 + 3x_2 \geq 16$.
- $4x_1 - 3x_2 \geq 0$.
- The total combined cardiovascular support index is at most 49: $11x_1 + 10x_2 \leq 49$.
- The total combined energy stability index is at most 50: $12x_1 + 3x_2 \leq 50$.

## 4: Create a symbolic representation of the problem
The symbolic representation is:
```json
{
'sym_variables': [('x1', 'milligrams of vitamin B12'), ('x2', 'milligrams of vitamin B7')],
'objective_function': '9.14*x1 + 5.53*x2',
'constraints': [
    '11*x1 + 10*x2 >= 23',
    '12*x1 + 3*x2 >= 16',
    '4*x1 - 3*x2 >= 0',
    '11*x1 + 10*x2 <= 49',
    '12*x1 + 3*x2 <= 50'
]
}
```

## 5: Write the Gurobi code to solve the optimization problem
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="x1", lb=0)  # milligrams of vitamin B12
    x2 = model.addVar(name="x2", lb=0)  # milligrams of vitamin B7

    # Define the objective function
    model.setObjective(9.14 * x1 + 5.53 * x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(11 * x1 + 10 * x2 >= 23, name="cardiovascular_support_index_min")
    model.addConstr(12 * x1 + 3 * x2 >= 16, name="energy_stability_index_min")
    model.addConstr(4 * x1 - 3 * x2 >= 0, name="vitamin_B12_B7_relation")
    model.addConstr(11 * x1 + 10 * x2 <= 49, name="cardiovascular_support_index_max")
    model.addConstr(12 * x1 + 3 * x2 <= 50, name="energy_stability_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 B12: {x1.varValue}")
        print(f"Milligrams of vitamin B7: {x2.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```