## Step 1: Define the optimization problem
The problem is to minimize the objective function: $9x_0 + 3x_1 + 8x_2 + 4x_3$, where $x_0$ represents milligrams of vitamin B9, $x_1$ represents milligrams of calcium, $x_2$ represents milligrams of vitamin B3, and $x_3$ represents milligrams of vitamin B2.

## Step 2: List all the constraints
The constraints are as follows:
- $7x_0 \leq 296$
- $2x_0 \leq 253$
- $11x_0 \leq 70$
- $4x_1 \leq 296$
- $5x_1 \leq 253$
- $3x_1 \leq 70$
- $10x_2 \leq 296$
- $x_2 \leq 253$
- $2x_2 \leq 70$
- $6x_3 \leq 296$
- $x_3 \leq 253$
- $10x_3 \leq 70$
- $4x_1 + 6x_3 \geq 60$
- $7x_0 + 6x_3 \geq 35$
- $7x_0 + 10x_2 \geq 67$
- $4x_1 + 10x_2 \geq 24$
- $7x_0 + 4x_1 + 6x_3 \geq 73$
- $7x_0 + 10x_2 + 6x_3 \geq 73$
- $4x_1 + 10x_2 + 6x_3 \geq 73$
- $7x_0 + 4x_1 + 6x_3 \geq 56$
- $7x_0 + 10x_2 + 6x_3 \geq 56$
- $4x_1 + 10x_2 + 6x_3 \geq 56$
- $7x_0 + 4x_1 + 6x_3 \geq 47$
- $7x_0 + 10x_2 + 6x_3 \geq 47$
- $4x_1 + 10x_2 + 6x_3 \geq 47$
- $7x_0 + 4x_1 + 10x_2 + 6x_3 \geq 47$
- $2x_0 + x_2 \geq 30$
- $2x_0 + x_3 \geq 37$
- $2x_0 + 5x_1 + x_3 \geq 41$
- $2x_0 + 5x_1 + x_2 + x_3 \geq 41$
- $3x_1 + 2x_2 \geq 15$
- $11x_0 + 10x_3 \geq 7$
- $11x_0 + 3x_1 \geq 10$
- $3x_1 + 2x_2 + 10x_3 \geq 12$
- $11x_0 + 3x_1 + 2x_2 \geq 12$
- $3x_1 + 2x_2 + 10x_3 \geq 12$
- $11x_0 + 3x_1 + 2x_2 \geq 12$
- $11x_0 + 3x_1 + 2x_2 + 10x_3 \geq 12$
- $4x_0 - 6x_1 \geq 0$
- $4x_1 - 4x_2 \geq 0$
- $4x_1 + 10x_2 \leq 265$
- $7x_0 + 6x_3 \leq 114$
- $10x_2 + 6x_3 \leq 142$
- $5x_1 + x_2 \leq 236$
- $2x_0 + x_2 \leq 196$
- $5x_1 + x_3 \leq 133$
- $11x_0 + 2x_2 \leq 20$

## Step 3: Implement the optimization problem using Gurobi
We will use the Gurobi library in Python to 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 B9", lb=-gp.GRB.INFINITY)
x1 = m.addVar(name="milligrams of calcium", lb=-gp.GRB.INFINITY)
x2 = m.addVar(name="milligrams of vitamin B3", lb=-gp.GRB.INFINITY)
x3 = m.addVar(name="milligrams of vitamin B2", lb=-gp.GRB.INFINITY)

# Define the objective function
m.setObjective(9 * x0 + 3 * x1 + 8 * x2 + 4 * x3, gp.GRB.MINIMIZE)

# Add constraints
m.addConstr(7 * x0 <= 296)
m.addConstr(2 * x0 <= 253)
m.addConstr(11 * x0 <= 70)
m.addConstr(4 * x1 <= 296)
m.addConstr(5 * x1 <= 253)
m.addConstr(3 * x1 <= 70)
m.addConstr(10 * x2 <= 296)
m.addConstr(x2 <= 253)
m.addConstr(2 * x2 <= 70)
m.addConstr(6 * x3 <= 296)
m.addConstr(x3 <= 253)
m.addConstr(10 * x3 <= 70)
m.addConstr(4 * x1 + 6 * x3 >= 60)
m.addConstr(7 * x0 + 6 * x3 >= 35)
m.addConstr(7 * x0 + 10 * x2 >= 67)
m.addConstr(4 * x1 + 10 * x2 >= 24)
m.addConstr(7 * x0 + 4 * x1 + 6 * x3 >= 73)
m.addConstr(7 * x0 + 10 * x2 + 6 * x3 >= 73)
m.addConstr(4 * x1 + 10 * x2 + 6 * x3 >= 73)
m.addConstr(7 * x0 + 4 * x1 + 6 * x3 >= 56)
m.addConstr(7 * x0 + 10 * x2 + 6 * x3 >= 56)
m.addConstr(4 * x1 + 10 * x2 + 6 * x3 >= 56)
m.addConstr(7 * x0 + 4 * x1 + 6 * x3 >= 47)
m.addConstr(7 * x0 + 10 * x2 + 6 * x3 >= 47)
m.addConstr(4 * x1 + 10 * x2 + 6 * x3 >= 47)
m.addConstr(7 * x0 + 4 * x1 + 10 * x2 + 6 * x3 >= 47)
m.addConstr(2 * x0 + x2 >= 30)
m.addConstr(2 * x0 + x3 >= 37)
m.addConstr(2 * x0 + 5 * x1 + x3 >= 41)
m.addConstr(2 * x0 + 5 * x1 + x2 + x3 >= 41)
m.addConstr(3 * x1 + 2 * x2 >= 15)
m.addConstr(11 * x0 + 10 * x3 >= 7)
m.addConstr(11 * x0 + 3 * x1 >= 10)
m.addConstr(3 * x1 + 2 * x2 + 10 * x3 >= 12)
m.addConstr(11 * x0 + 3 * x1 + 2 * x2 >= 12)
m.addConstr(3 * x1 + 2 * x2 + 10 * x3 >= 12)
m.addConstr(11 * x0 + 3 * x1 + 2 * x2 >= 12)
m.addConstr(11 * x0 + 3 * x1 + 2 * x2 + 10 * x3 >= 12)
m.addConstr(4 * x0 - 6 * x1 >= 0)
m.addConstr(4 * x1 - 4 * x2 >= 0)
m.addConstr(4 * x1 + 10 * x2 <= 265)
m.addConstr(7 * x0 + 6 * x3 <= 114)
m.addConstr(10 * x2 + 6 * x3 <= 142)
m.addConstr(5 * x1 + x2 <= 236)
m.addConstr(2 * x0 + x2 <= 196)
m.addConstr(5 * x1 + x3 <= 133)
m.addConstr(11 * x0 + 2 * x2 <= 20)

# Solve the model
m.optimize()

# Print the solution
if m.status == gp.GRB.OPTIMAL:
    print("Optimal solution found.")
    print("Milligrams of vitamin B9: ", x0.varValue)
    print("Milligrams of calcium: ", x1.varValue)
    print("Milligrams of vitamin B3: ", x2.varValue)
    print("Milligrams of vitamin B2: ", x3.varValue)
    print("Objective function value: ", m.objVal)
else:
    print("No optimal solution found.")
```