## Step 1: Define the optimization problem
The problem is to maximize the objective function: $2x_0 + 6x_1 + 4x_2 + 4x_3$, where $x_0$ represents milligrams of vitamin B12, $x_1$ represents milligrams of magnesium, $x_2$ represents milligrams of vitamin E, and $x_3$ represents milligrams of vitamin B5.

## Step 2: List all the constraints
The constraints are as follows:
- $6x_0 \leq 110$
- $1x_0 \leq 110$
- $8x_0 \leq 88$
- $4x_0 \leq 103$
- $3x_1 \leq 110$
- $8x_1 \leq 110$
- $4x_1 \leq 88$
- $5x_1 \leq 103$
- $8x_2 \leq 110$
- $3x_2 \leq 110$
- $8x_2 \leq 88$
- $10x_2 \leq 103$
- $1x_3 \leq 110$
- $6x_3 \leq 110$
- $9x_3 \leq 88$
- $4x_3 \leq 103$
- $3x_1 + 8x_2 + 1x_3 \geq 25$
- $3x_2 + 6x_3 \geq 10$
- $4x_0 + 5x_1 \geq 11$
- $4x_0 + 10x_2 \geq 22$
- $10x_2 + 4x_3 \geq 25$
- $4x_0 + 4x_3 \geq 9$
- $4x_0 + 10x_2 + 4x_3 \geq 22$
- $2x_0 - 6x_2 \geq 0$
- $3x_1 + 8x_2 \leq 104$
- $3x_1 + 8x_2 + 1x_3 \leq 107$
- $6x_0 + 8x_2 + 1x_3 \leq 50$
- $6x_0 + 3x_1 + 1x_3 \leq 104$
- $6x_0 + 3x_1 + 8x_2 \leq 71$
- $6x_0 + 3x_1 + 8x_2 + 1x_3 \leq 71$
- $1x_0 + 8x_1 \leq 104$
- $1x_0 + 6x_3 \leq 80$
- $8x_1 + 6x_3 \leq 35$
- $1x_0 + 3x_2 \leq 86$
- $8x_1 + 3x_2 \leq 57$
- $1x_0 + 8x_1 + 6x_3 \leq 51$
- $1x_0 + 8x_1 + 3x_2 + 6x_3 \leq 51$
- $8x_0 + 9x_3 \leq 69$
- $4x_1 + 8x_2 \leq 67$
- $8x_2 + 9x_3 \leq 35$
- $8x_0 + 4x_1 \leq 60$
- $8x_0 + 4x_1 + 8x_2 \leq 82$
- $8x_0 + 4x_1 + 8x_2 + 9x_3 \leq 82$
- $4x_0 + 4x_3 \leq 39$
- $4x_0 + 5x_1 + 10x_2 + 4x_3 \leq 39$

## Step 3: Implement the optimization problem using Gurobi
We will use the Gurobi Python library to model and solve this optimization problem.

```python
import gurobi as gp

# Create a new model
m = gp.Model("optimization_problem")

# Define the variables
x0 = m.addVar(name="milligrams of vitamin B12", lb=-gp.GRB.INFINITY, ub=gp.GRB.INFINITY)
x1 = m.addVar(name="milligrams of magnesium", lb=-gp.GRB.INFINITY, ub=gp.GRB.INFINITY)
x2 = m.addVar(name="milligrams of vitamin E", lb=-gp.GRB.INFINITY, ub=gp.GRB.INFINITY)
x3 = m.addVar(name="milligrams of vitamin B5", lb=-gp.GRB.INFINITY, ub=gp.GRB.INFINITY)

# Define the objective function
m.setObjective(2*x0 + 6*x1 + 4*x2 + 4*x3, gp.GRB.MAXIMIZE)

# Add constraints
m.addConstr(6*x0 <= 110)
m.addConstr(1*x0 <= 110)
m.addConstr(8*x0 <= 88)
m.addConstr(4*x0 <= 103)
m.addConstr(3*x1 <= 110)
m.addConstr(8*x1 <= 110)
m.addConstr(4*x1 <= 88)
m.addConstr(5*x1 <= 103)
m.addConstr(8*x2 <= 110)
m.addConstr(3*x2 <= 110)
m.addConstr(8*x2 <= 88)
m.addConstr(10*x2 <= 103)
m.addConstr(1*x3 <= 110)
m.addConstr(6*x3 <= 110)
m.addConstr(9*x3 <= 88)
m.addConstr(4*x3 <= 103)
m.addConstr(3*x1 + 8*x2 + 1*x3 >= 25)
m.addConstr(3*x2 + 6*x3 >= 10)
m.addConstr(4*x0 + 5*x1 >= 11)
m.addConstr(4*x0 + 10*x2 >= 22)
m.addConstr(10*x2 + 4*x3 >= 25)
m.addConstr(4*x0 + 4*x3 >= 9)
m.addConstr(4*x0 + 10*x2 + 4*x3 >= 22)
m.addConstr(2*x0 - 6*x2 >= 0)
m.addConstr(3*x1 + 8*x2 <= 104)
m.addConstr(3*x1 + 8*x2 + 1*x3 <= 107)
m.addConstr(6*x0 + 8*x2 + 1*x3 <= 50)
m.addConstr(6*x0 + 3*x1 + 1*x3 <= 104)
m.addConstr(6*x0 + 3*x1 + 8*x2 <= 71)
m.addConstr(6*x0 + 3*x1 + 8*x2 + 1*x3 <= 71)
m.addConstr(1*x0 + 8*x1 <= 104)
m.addConstr(1*x0 + 6*x3 <= 80)
m.addConstr(8*x1 + 6*x3 <= 35)
m.addConstr(1*x0 + 3*x2 <= 86)
m.addConstr(8*x1 + 3*x2 <= 57)
m.addConstr(1*x0 + 8*x1 + 6*x3 <= 51)
m.addConstr(1*x0 + 8*x1 + 3*x2 + 6*x3 <= 51)
m.addConstr(8*x0 + 9*x3 <= 69)
m.addConstr(4*x1 + 8*x2 <= 67)
m.addConstr(8*x2 + 9*x3 <= 35)
m.addConstr(8*x0 + 4*x1 <= 60)
m.addConstr(8*x0 + 4*x1 + 8*x2 <= 82)
m.addConstr(8*x0 + 4*x1 + 8*x2 + 9*x3 <= 82)
m.addConstr(4*x0 + 4*x3 <= 39)
m.addConstr(4*x0 + 5*x1 + 10*x2 + 4*x3 <= 39)

# Solve the model
m.optimize()

# Print the solution
if m.status == gp.GRB.OPTIMAL:
    print("Optimal solution found.")
    print("Milligrams of vitamin B12: ", x0.varValue)
    print("Milligrams of magnesium: ", x1.varValue)
    print("Milligrams of vitamin E: ", x2.varValue)
    print("Milligrams of vitamin B5: ", x3.varValue)
    print("Objective function value: ", m.objVal)
else:
    print("No optimal solution found.")
```