## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'milligrams of vitamin B7' and 'milligrams of vitamin B5'. Let's denote 'milligrams of vitamin B7' as $x_1$ and 'milligrams of vitamin B5' as $x_2$. The objective function to minimize is $8x_1 + 8x_2$.

## Step 2: List the constraints
The constraints given are:
- $6x_1 \leq 39$
- $3x_1 \leq 42$
- $8x_1 \leq 18$
- $4x_1 \leq 65$
- $2x_1 \leq 39$
- $6x_2 \leq 39$
- $3x_2 \leq 42$
- $1x_2 \leq 18$
- $5x_2 \leq 65$
- $2x_2 \leq 39$
- $6x_1 + 6x_2 \geq 15$
- $3x_1 + 3x_2 \geq 21$
- $8x_1 + 1x_2 \geq 9$
- $4x_1 + 5x_2 \geq 25$
- $2x_1 + 2x_2 \geq 15$
- $-3x_1 + 8x_2 \geq 0$
- $6x_1 + 6x_2 \leq 24$
- $3x_1 + 3x_2 \leq 25$
- $8x_1 + 1x_2 \leq 12$
- $4x_1 + 5x_2 \leq 35$
- $2x_1 + 2x_2 \leq 24$

## 3: Convert the problem into a symbolic representation
The symbolic representation of the problem is:
```json
{
'sym_variables': [('x1', 'milligrams of vitamin B7'), ('x2', 'milligrams of vitamin B5')],
'objective_function': '8*x1 + 8*x2',
'constraints': [
'6*x1 <= 39',
'3*x1 <= 42',
'8*x1 <= 18',
'4*x1 <= 65',
'2*x1 <= 39',
'6*x2 <= 39',
'3*x2 <= 42',
'1*x2 <= 18',
'5*x2 <= 65',
'2*x2 <= 39',
'6*x1 + 6*x2 >= 15',
'3*x1 + 3*x2 >= 21',
'8*x1 + x2 >= 9',
'4*x1 + 5*x2 >= 25',
'2*x1 + 2*x2 >= 15',
'-3*x1 + 8*x2 >= 0',
'6*x1 + 6*x2 <= 24',
'3*x1 + 3*x2 <= 25',
'8*x1 + x2 <= 12',
'4*x1 + 5*x2 <= 35',
'2*x1 + 2*x2 <= 24'
]
}
```

## 4: Implement the problem in Gurobi
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="x1", lb=0)  # milligrams of vitamin B7
    x2 = model.addVar(name="x2", lb=0)  # milligrams of vitamin B5

    # Define the objective function
    model.setObjective(8 * x1 + 8 * x2, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(6 * x1 <= 39)
    model.addConstr(3 * x1 <= 42)
    model.addConstr(8 * x1 <= 18)
    model.addConstr(4 * x1 <= 65)
    model.addConstr(2 * x1 <= 39)
    model.addConstr(6 * x2 <= 39)
    model.addConstr(3 * x2 <= 42)
    model.addConstr(x2 <= 18)
    model.addConstr(5 * x2 <= 65)
    model.addConstr(2 * x2 <= 39)
    model.addConstr(6 * x1 + 6 * x2 >= 15)
    model.addConstr(3 * x1 + 3 * x2 >= 21)
    model.addConstr(8 * x1 + x2 >= 9)
    model.addConstr(4 * x1 + 5 * x2 >= 25)
    model.addConstr(2 * x1 + 2 * x2 >= 15)
    model.addConstr(-3 * x1 + 8 * x2 >= 0)
    model.addConstr(6 * x1 + 6 * x2 <= 24)
    model.addConstr(3 * x1 + 3 * x2 <= 25)
    model.addConstr(8 * x1 + x2 <= 12)
    model.addConstr(4 * x1 + 5 * x2 <= 35)
    model.addConstr(2 * x1 + 2 * x2 <= 24)

    # 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 vitamin B5: {x2.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```