## Step 1: Define the symbolic representation of the variables
The variables are defined as follows: 
- $x_1$ represents 'milligrams of vitamin D'
- $x_2$ represents 'milligrams of vitamin B1'
- $x_3$ represents 'milligrams of zinc'
- $x_4$ represents 'milligrams of vitamin B5'

## Step 2: Convert the objective function into symbolic notation
The objective function to maximize is $2.02x_1 + 6.45x_2 + 5.51x_3 + 2.96x_4$.

## Step 3: Define the constraints in symbolic notation
The constraints are:
- $14x_1 + 6x_2 \geq 24$
- $14x_1 + 16x_4 \geq 23$
- $14x_1 + 15x_3 \leq 75$
- $6x_2 + 15x_3 \leq 77$
- $15x_3 + 16x_4 \leq 63$
- $14x_1 + 6x_2 + 15x_3 + 16x_4 \leq 112$

## 4: Provide the symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x1', 'milligrams of vitamin D'), 
        ('x2', 'milligrams of vitamin B1'), 
        ('x3', 'milligrams of zinc'), 
        ('x4', 'milligrams of vitamin B5')
    ], 
    'objective_function': '2.02*x1 + 6.45*x2 + 5.51*x3 + 2.96*x4', 
    'constraints': [
        '14*x1 + 6*x2 >= 24', 
        '14*x1 + 16*x4 >= 23', 
        '14*x1 + 15*x3 <= 75', 
        '6*x2 + 15*x3 <= 77', 
        '15*x3 + 16*x4 <= 63', 
        '14*x1 + 6*x2 + 15*x3 + 16*x4 <= 112'
    ]
}
```

## 5: 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(name="milligrams of vitamin D", lb=-float('inf'))
    x2 = model.addVar(name="milligrams of vitamin B1", lb=-float('inf'))
    x3 = model.addVar(name="milligrams of zinc", lb=-float('inf'))
    x4 = model.addVar(name="milligrams of vitamin B5", lb=-float('inf'))

    # Define the objective function
    model.setObjective(2.02*x1 + 6.45*x2 + 5.51*x3 + 2.96*x4, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(14*x1 + 6*x2 >= 24)
    model.addConstr(14*x1 + 16*x4 >= 23)
    model.addConstr(14*x1 + 15*x3 <= 75)
    model.addConstr(6*x2 + 15*x3 <= 77)
    model.addConstr(15*x3 + 16*x4 <= 63)
    model.addConstr(14*x1 + 6*x2 + 15*x3 + 16*x4 <= 112)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of vitamin D: {x1.varValue}")
        print(f"Milligrams of vitamin B1: {x2.varValue}")
        print(f"Milligrams of zinc: {x3.varValue}")
        print(f"Milligrams of vitamin B5: {x4.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```