## Step 1: Define the symbolic representation of the variables
The variables are defined as follows:
- $x_1$ represents 'milligrams of vitamin B7'
- $x_2$ represents 'milligrams of calcium'
- $x_3$ represents 'milligrams of vitamin D'
- $x_4$ represents 'milligrams of vitamin B4'

## Step 2: Convert the objective function into a symbolic representation
The objective function to minimize is $2.48x_1 + 5.52x_2 + 7.38x_3 + 1.06x_4$.

## Step 3: Define the constraints in symbolic notation
The constraints are:
1. $11.54x_1 \geq 0$ (implicit, as $x_1 \geq 0$)
2. $6.22x_2 \geq 0$ (implicit, as $x_2 \geq 0$)
3. $6.28x_3 \geq 0$ (implicit, as $x_3 \geq 0$)
4. $0.78x_4 \geq 0$ (implicit, as $x_4 \geq 0$)
5. $11.54x_1 + 0.78x_4 \geq 15$
6. $6.22x_2 + 6.28x_3 \geq 23$
7. $11.54x_1 + 6.28x_3 \geq 36$
8. $6.28x_3 + 0.78x_4 \geq 36$
9. $11.54x_1 + 6.22x_2 \geq 21$
10. $11.54x_1 + 6.22x_2 + 6.28x_3 + 0.78x_4 \geq 21$
11. $-5x_2 + 6x_4 \geq 0$
12. $11.54x_1 + 6.22x_2 \leq 121$
13. $6.22x_2 + 6.28x_3 \leq 51$
14. $11.54x_1 + 6.28x_3 \leq 46$
15. $x_1$ is an integer
16. $x_2$ is a float
17. $x_3$ is a float
18. $x_4$ is a float

## Step 4: Provide the symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x1', 'milligrams of vitamin B7'),
        ('x2', 'milligrams of calcium'),
        ('x3', 'milligrams of vitamin D'),
        ('x4', 'milligrams of vitamin B4')
    ],
    'objective_function': '2.48x1 + 5.52x2 + 7.38x3 + 1.06x4',
    'constraints': [
        '11.54x1 + 0.78x4 >= 15',
        '6.22x2 + 6.28x3 >= 23',
        '11.54x1 + 6.28x3 >= 36',
        '6.28x3 + 0.78x4 >= 36',
        '11.54x1 + 6.22x2 >= 21',
        '11.54x1 + 6.22x2 + 6.28x3 + 0.78x4 >= 21',
        '-5x2 + 6x4 >= 0',
        '11.54x1 + 6.22x2 <= 121',
        '6.22x2 + 6.28x3 <= 51',
        '11.54x1 + 6.28x3 <= 46'
    ]
}
```

## Step 5: Write the Gurobi code for the optimization problem
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name='x1', vtype=gurobi.GRB.INTEGER)  # milligrams of vitamin B7
    x2 = model.addVar(name='x2')  # milligrams of calcium
    x3 = model.addVar(name='x3')  # milligrams of vitamin D
    x4 = model.addVar(name='x4')  # milligrams of vitamin B4

    # Define the objective function
    model.setObjective(2.48 * x1 + 5.52 * x2 + 7.38 * x3 + 1.06 * x4, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(11.54 * x1 + 0.78 * x4 >= 15)
    model.addConstr(6.22 * x2 + 6.28 * x3 >= 23)
    model.addConstr(11.54 * x1 + 6.28 * x3 >= 36)
    model.addConstr(6.28 * x3 + 0.78 * x4 >= 36)
    model.addConstr(11.54 * x1 + 6.22 * x2 >= 21)
    model.addConstr(11.54 * x1 + 6.22 * x2 + 6.28 * x3 + 0.78 * x4 >= 21)
    model.addConstr(-5 * x2 + 6 * x4 >= 0)
    model.addConstr(11.54 * x1 + 6.22 * x2 <= 121)
    model.addConstr(6.22 * x2 + 6.28 * x3 <= 51)
    model.addConstr(11.54 * x1 + 6.28 * x3 <= 46)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print('Optimal solution found.')
        print(f'milligrams of vitamin B7: {x1.varValue}')
        print(f'milligrams of calcium: {x2.varValue}')
        print(f'milligrams of vitamin D: {x3.varValue}')
        print(f'milligrams of vitamin B4: {x4.varValue}')
        print(f'Objective function value: {model.objVal}')
    else:
        print('No optimal solution found.')

solve_optimization_problem()
```