## Step 1: Define the optimization problem
The problem is to maximize the objective function: $5.28x_0 + 4.71x_1 + 8.96x_2 + 6.81x_3$, where $x_0$ represents milligrams of magnesium, $x_1$ represents milligrams of vitamin B2, $x_2$ represents milligrams of vitamin C, and $x_3$ represents milligrams of vitamin B6.

## Step 2: List all the constraints
The constraints are as follows:
- $4.9x_0 \leq 235$
- $5.9x_0 \leq 196$
- $4.36x_1 \leq 235$
- $3.03x_1 \leq 196$
- $4.7x_2 \leq 235$
- $4.5x_2 \leq 196$
- $5.86x_3 \leq 235$
- $1.5x_3 \leq 196$
- $4.9x_0 + 4.7x_2 \geq 37$
- $4.36x_1 + 4.7x_2 \geq 26$
- $3.03x_1 + 4.5x_2 \geq 36$
- $4.9x_0 + 4.36x_1 \leq 101$
- $4.9x_0 + 5.86x_3 \leq 96$
- $4.7x_2 + 5.86x_3 \leq 69$
- $4.36x_1 + 5.86x_3 \leq 141$
- $4.9x_0 + 4.7x_2 + 5.86x_3 \leq 185$
- $4.9x_0 + 4.36x_1 + 5.86x_3 \leq 100$
- $4.36x_1 + 4.7x_2 + 5.86x_3 \leq 157$
- $4.9x_0 + 4.36x_1 + 4.7x_2 + 5.86x_3 \leq 157$
- $5.9x_0 + 1.5x_3 \leq 71$
- $4.5x_2 + 1.5x_3 \leq 71$
- $3.03x_1 + 1.5x_3 \leq 186$
- $5.9x_0 + 3.03x_1 \leq 188$
- $3.03x_1 + 4.5x_2 \leq 87$
- $5.9x_0 + 4.5x_2 \leq 164$
- $5.9x_0 + 3.03x_1 + 1.5x_3 \leq 157$
- $5.9x_0 + 3.03x_1 + 4.5x_2 \leq 150$
- $5.9x_0 + 3.03x_1 + 4.5x_2 + 1.5x_3 \leq 150$

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

```python
import gurobi

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

    # Define the variables
    x0 = model.addVar(name="milligrams_of_magnesium", lb=0)
    x1 = model.addVar(name="milligrams_of_vitamin_B2", lb=0)
    x2 = model.addVar(name="milligrams_of_vitamin_C", lb=0)
    x3 = model.addVar(name="milligrams_of_vitamin_B6", lb=0)

    # Define the objective function
    model.setObjective(5.28*x0 + 4.71*x1 + 8.96*x2 + 6.81*x3, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(4.9*x0 <= 235)
    model.addConstr(5.9*x0 <= 196)
    model.addConstr(4.36*x1 <= 235)
    model.addConstr(3.03*x1 <= 196)
    model.addConstr(4.7*x2 <= 235)
    model.addConstr(4.5*x2 <= 196)
    model.addConstr(5.86*x3 <= 235)
    model.addConstr(1.5*x3 <= 196)
    model.addConstr(4.9*x0 + 4.7*x2 >= 37)
    model.addConstr(4.36*x1 + 4.7*x2 >= 26)
    model.addConstr(3.03*x1 + 4.5*x2 >= 36)
    model.addConstr(4.9*x0 + 4.36*x1 <= 101)
    model.addConstr(4.9*x0 + 5.86*x3 <= 96)
    model.addConstr(4.7*x2 + 5.86*x3 <= 69)
    model.addConstr(4.36*x1 + 5.86*x3 <= 141)
    model.addConstr(4.9*x0 + 4.7*x2 + 5.86*x3 <= 185)
    model.addConstr(4.9*x0 + 4.36*x1 + 5.86*x3 <= 100)
    model.addConstr(4.36*x1 + 4.7*x2 + 5.86*x3 <= 157)
    model.addConstr(4.9*x0 + 4.36*x1 + 4.7*x2 + 5.86*x3 <= 157)
    model.addConstr(5.9*x0 + 1.5*x3 <= 71)
    model.addConstr(4.5*x2 + 1.5*x3 <= 71)
    model.addConstr(3.03*x1 + 1.5*x3 <= 186)
    model.addConstr(5.9*x0 + 3.03*x1 <= 188)
    model.addConstr(3.03*x1 + 4.5*x2 <= 87)
    model.addConstr(5.9*x0 + 4.5*x2 <= 164)
    model.addConstr(5.9*x0 + 3.03*x1 + 1.5*x3 <= 157)
    model.addConstr(5.9*x0 + 3.03*x1 + 4.5*x2 <= 150)
    model.addConstr(5.9*x0 + 3.03*x1 + 4.5*x2 + 1.5*x3 <= 150)

    # Optimize the model
    model.optimize()

    # Print the status of the optimization
    if model.status == gurobi.GRB.Status.OPTIMAL:
        print("Optimal solution found.")
        print("Milligrams of magnesium: ", x0.varValue)
        print("Milligrams of vitamin B2: ", x1.varValue)
        print("Milligrams of vitamin C: ", x2.varValue)
        print("Milligrams of vitamin B6: ", x3.varValue)
        print("Objective function value: ", model.objVal)
    elif model.status == gurobi.GRB.Status.INFEASIBLE:
        print("No feasible solution found.")
    else:
        print("Optimization failed.")

optimize()
```