## Step 1: Define the symbolic representation of the variables
The variables are ['milligrams of vitamin B5', 'milligrams of vitamin B3', 'milligrams of vitamin B2'] which can be represented symbolically as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables is:
- $x_0$ : milligrams of vitamin B5
- $x_1$ : milligrams of vitamin B3
- $x_2$ : milligrams of vitamin B2

## Step 3: Define the objective function in symbolic notation
The objective function to minimize is $6x_0 + 3x_1 + 5x_2$.

## Step 4: List the constraints in symbolic notation
The constraints are:
- $0.06x_0 \leq 50$
- $1.05x_0 \leq 66$
- $8.57x_0 \leq 126$
- $8.56x_1 \leq 50$
- $7.92x_1 \leq 66$
- $7.38x_1 \leq 126$
- $0.26x_2 \leq 50$
- $4.16x_2 \leq 66$
- $6.67x_2 \leq 126$
- $0.06x_0 + 0.26x_2 \geq 11$
- $0.06x_0 + 8.56x_1 + 0.26x_2 \geq 15$
- $1.05x_0 + 7.92x_1 \geq 11$
- $1.05x_0 + 7.92x_1 + 4.16x_2 \geq 12$
- $8.57x_0 + 7.38x_1 \geq 25$
- $8.57x_0 + 7.38x_1 + 6.67x_2 \geq 25$
- $2x_0 - 9x_2 \geq 0$
- $8.56x_1 + 0.26x_2 \leq 43$
- $0.06x_0 + 0.26x_2 \leq 19$
- $1.05x_0 + 7.92x_1 + 4.16x_2 \leq 58$
- $x_1 \in \mathbb{Z}$ (integer constraint for $x_1$)
- $x_2 \in \mathbb{Z}$ (integer constraint for $x_2$)

## 5: Provide the symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x0', 'milligrams of vitamin B5'), 
        ('x1', 'milligrams of vitamin B3'), 
        ('x2', 'milligrams of vitamin B2')
    ], 
    'objective_function': '6*x0 + 3*x1 + 5*x2', 
    'constraints': [
        '0.06*x0 <= 50',
        '1.05*x0 <= 66',
        '8.57*x0 <= 126',
        '8.56*x1 <= 50',
        '7.92*x1 <= 66',
        '7.38*x1 <= 126',
        '0.26*x2 <= 50',
        '4.16*x2 <= 66',
        '6.67*x2 <= 126',
        '0.06*x0 + 0.26*x2 >= 11',
        '0.06*x0 + 8.56*x1 + 0.26*x2 >= 15',
        '1.05*x0 + 7.92*x1 >= 11',
        '1.05*x0 + 7.92*x1 + 4.16*x2 >= 12',
        '8.57*x0 + 7.38*x1 >= 25',
        '8.57*x0 + 7.38*x1 + 6.67*x2 >= 25',
        '2*x0 - 9*x2 >= 0',
        '8.56*x1 + 0.26*x2 <= 43',
        '0.06*x0 + 0.26*x2 <= 19',
        '1.05*x0 + 7.92*x1 + 4.16*x2 <= 58'
    ]
}
```

## 6: Implement the optimization problem using Gurobi
```python
import gurobi

# Create a new model
m = gurobi.Model()

# Define the variables
x0 = m.addVar(name="x0", lb=0)  # milligrams of vitamin B5
x1 = m.addVar(name="x1", lb=0, integrality=1)  # milligrams of vitamin B3
x2 = m.addVar(name="x2", lb=0, integrality=1)  # milligrams of vitamin B2

# Objective function
m.setObjective(6 * x0 + 3 * x1 + 5 * x2, gurobi.GRB.MINIMIZE)

# Constraints
m.addConstr(0.06 * x0 <= 50)
m.addConstr(1.05 * x0 <= 66)
m.addConstr(8.57 * x0 <= 126)
m.addConstr(8.56 * x1 <= 50)
m.addConstr(7.92 * x1 <= 66)
m.addConstr(7.38 * x1 <= 126)
m.addConstr(0.26 * x2 <= 50)
m.addConstr(4.16 * x2 <= 66)
m.addConstr(6.67 * x2 <= 126)
m.addConstr(0.06 * x0 + 0.26 * x2 >= 11)
m.addConstr(0.06 * x0 + 8.56 * x1 + 0.26 * x2 >= 15)
m.addConstr(1.05 * x0 + 7.92 * x1 >= 11)
m.addConstr(1.05 * x0 + 7.92 * x1 + 4.16 * x2 >= 12)
m.addConstr(8.57 * x0 + 7.38 * x1 >= 25)
m.addConstr(8.57 * x0 + 7.38 * x1 + 6.67 * x2 >= 25)
m.addConstr(2 * x0 - 9 * x2 >= 0)
m.addConstr(8.56 * x1 + 0.26 * x2 <= 43)
m.addConstr(0.06 * x0 + 0.26 * x2 <= 19)
m.addConstr(1.05 * x0 + 7.92 * x1 + 4.16 * x2 <= 58)

# Solve the model
m.optimize()

# Print the solution
if m.status == gurobi.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"x0 (milligrams of vitamin B5): {x0.varValue}")
    print(f"x1 (milligrams of vitamin B3): {x1.varValue}")
    print(f"x2 (milligrams of vitamin B2): {x2.varValue}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found.")
```