## Step 1: Define the symbolic representation of the variables
The variables are defined as follows: 
- $x_0$ represents 'milligrams of magnesium'
- $x_1$ represents 'milligrams of vitamin B12'
- $x_2$ represents 'milligrams of calcium'
- $x_3$ represents 'milligrams of vitamin B1'

## Step 2: Define the objective function in symbolic notation
The objective function to minimize is $2x_0 + x_1 + 6x_2 + 6x_3$.

## Step 3: List all the constraints in symbolic notation
Constraints:
- $7x_0 \leq 153$
- $x_0 \geq 0$
- $1x_0 \leq 266$
- $2x_0 \leq 186$
- $8x_0 \leq 145$
- $10x_1 \leq 153$
- $6x_1 \leq 266$
- $10x_1 \leq 186$
- $13x_1 \leq 145$
- $14x_2 \leq 153$
- $5x_2 \leq 266$
- $4x_2 \leq 186$
- $2x_2 \leq 145$
- $3x_3 \leq 153$
- $11x_3 \leq 266$
- $14x_3 \leq 186$
- $2x_3 \leq 145$
- $7x_0 + 10x_1 \geq 21$
- $7x_0 + 3x_3 \geq 21$
- $10x_1 + 3x_3 \geq 31$
- $7x_0 + 14x_2 + 3x_3 \geq 23$
- $7x_0 + 10x_1 + 14x_2 \geq 23$
- $7x_0 + 14x_2 + 3x_3 \geq 19$
- $7x_0 + 10x_1 + 14x_2 \geq 19$
- $7x_0 + 10x_1 + 14x_2 + 3x_3 \geq 19$
- $1x_0 + 5x_2 \geq 37$
- $6x_1 + 11x_3 \geq 64$
- $1x_0 + 6x_1 \geq 22$
- $6x_1 + 5x_2 \geq 61$
- $1x_0 + 11x_3 \geq 33$
- $5x_2 + 11x_3 \geq 31$
- $1x_0 + 6x_1 + 11x_3 \geq 62$
- $1x_0 + 6x_1 + 5x_2 \geq 62$
- $6x_1 + 5x_2 + 11x_3 \geq 62$
- $1x_0 + 6x_1 + 11x_3 \geq 52$
- $1x_0 + 6x_1 + 5x_2 \geq 52$
- $6x_1 + 5x_2 + 11x_3 \geq 52$
- $1x_0 + 6x_1 + 11x_3 \geq 66$
- $1x_0 + 6x_1 + 5x_2 \geq 66$
- $6x_1 + 5x_2 + 11x_3 \geq 66$
- $1x_0 + 6x_1 + 5x_2 + 11x_3 \geq 66$
- $4x_2 + 14x_3 \geq 41$
- $2x_0 + 10x_1 \geq 20$
- $10x_1 + 14x_3 \geq 21$
- $2x_0 + 4x_2 + 14x_3 \geq 34$
- $2x_0 + 10x_1 + 14x_3 \geq 34$
- $2x_0 + 4x_2 + 14x_3 \geq 38$
- $2x_0 + 10x_1 + 14x_3 \geq 38$
- $2x_0 + 4x_2 + 14x_3 + 14x_3 \geq 38$
- $2x_0 + 10x_1 + 4x_2 + 14x_3 \geq 38$
- $2x_0 + 2x_2 + 14x_3 \geq 21$
- $10x_1 + 14x_3 \geq 26$
- $10x_1 + 2x_2 \geq 14$
- $2x_0 + 2x_2 + 14x_3 \geq 31$
- $2x_0 + 10x_1 + 2x_2 + 14x_3 \geq 31$
- $x_0 - 10x_1 \geq 0$
- $7x_0 + 3x_3 \leq 99$
- $10x_1 + 14x_2 \leq 80$
- $1x_0 + 11x_3 \leq 72$
- $1x_0 + 6x_1 \leq 175$
- $6x_1 + 5x_2 \leq 150$
- $10x_1 + 14x_3 \leq 164$
- $4x_2 + 14x_3 \leq 86$
- $2x_0 + 14x_3 \leq 130$
- $2x_0 + 10x_1 \leq 116$
- $2x_0 + 10x_1 + 4x_2 \leq 185$
- $2x_0 + 14x_3 \leq 73$
- $2x_0 + 10x_1 \leq 40$
- $10x_1 + 14x_3 \leq 111$
- $2x_0 + 2x_2 \leq 102$
- $2x_0 + 10x_1 + 14x_3 \leq 82$

## 4: Write the Gurobi code
```python
import gurobi

def solve_optimization_problem():
    model = gurobi.Model()

    # Define variables
    x0 = model.addVar(name="milligrams of magnesium", lb=0)
    x1 = model.addVar(name="milligrams of vitamin B12", lb=0)
    x2 = model.addVar(name="milligrams of calcium", lb=0, integrality=1)
    x3 = model.addVar(name="milligrams of vitamin B1", lb=0)

    # Objective function
    model.setObjective(2*x0 + x1 + 6*x2 + 6*x3, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(7*x0 <= 153)
    model.addConstr(x0 >= 0)
    model.addConstr(x0 <= 266/1)
    model.addConstr(2*x0 <= 186)
    model.addConstr(8*x0 <= 145)

    model.addConstr(10*x1 <= 153)
    model.addConstr(6*x1 <= 266)
    model.addConstr(10*x1 <= 186)
    model.addConstr(13*x1 <= 145)

    model.addConstr(14*x2 <= 153)
    model.addConstr(5*x2 <= 266)
    model.addConstr(4*x2 <= 186)
    model.addConstr(2*x2 <= 145)

    model.addConstr(3*x3 <= 153)
    model.addConstr(11*x3 <= 266)
    model.addConstr(14*x3 <= 186)
    model.addConstr(2*x3 <= 145)

    model.addConstr(7*x0 + 10*x1 >= 21)
    model.addConstr(7*x0 + 3*x3 >= 21)
    model.addConstr(10*x1 + 3*x3 >= 31)
    model.addConstr(7*x0 + 14*x2 + 3*x3 >= 23)
    model.addConstr(7*x0 + 10*x1 + 14*x2 >= 23)
    model.addConstr(7*x0 + 14*x2 + 3*x3 >= 19)
    model.addConstr(7*x0 + 10*x1 + 14*x2 >= 19)
    model.addConstr(7*x0 + 10*x1 + 14*x2 + 3*x3 >= 19)

    model.addConstr(x0 + 5*x2 >= 37)
    model.addConstr(6*x1 + 11*x3 >= 64)
    model.addConstr(x0 + 6*x1 >= 22)
    model.addConstr(6*x1 + 5*x2 >= 61)
    model.addConstr(x0 + 11*x3 >= 33)
    model.addConstr(5*x2 + 11*x3 >= 31)
    model.addConstr(x0 + 6*x1 + 11*x3 >= 62)
    model.addConstr(x0 + 6*x1 + 5*x2 >= 62)
    model.addConstr(6*x1 + 5*x2 + 11*x3 >= 62)
    model.addConstr(x0 + 6*x1 + 11*x3 >= 52)
    model.addConstr(x0 + 6*x1 + 5*x2 >= 52)
    model.addConstr(6*x1 + 5*x2 + 11*x3 >= 52)
    model.addConstr(x0 + 6*x1 + 11*x3 >= 66)
    model.addConstr(x0 + 6*x1 + 5*x2 >= 66)
    model.addConstr(6*x1 + 5*x2 + 11*x3 >= 66)
    model.addConstr(x0 + 6*x1 + 5*x2 + 11*x3 >= 66)

    model.addConstr(4*x2 + 14*x3 >= 41)
    model.addConstr(2*x0 + 10*x1 >= 20)
    model.addConstr(10*x1 + 14*x3 >= 21)
    model.addConstr(2*x0 + 4*x2 + 14*x3 >= 34)
    model.addConstr(2*x0 + 10*x1 + 14*x3 >= 34)
    model.addConstr(2*x0 + 4*x2 + 14*x3 >= 38)
    model.addConstr(2*x0 + 10*x1 + 14*x3 >= 38)
    model.addConstr(2*x0 + 4*x2 + 14*x3 >= 38)
    model.addConstr(2*x0 + 10*x1 + 4*x2 + 14*x3 >= 38)

    model.addConstr(2*x0 + 2*x2 + 14*x3 >= 21)
    model.addConstr(10*x1 + 14*x3 >= 26)
    model.addConstr(10*x1 + 2*x2 >= 14)
    model.addConstr(2*x0 + 2*x2 + 14*x3 >= 31)
    model.addConstr(2*x0 + 10*x1 + 2*x2 + 14*x3 >= 31)

    model.addConstr(x0 - 10*x1 >= 0)

    model.addConstr(7*x0 + 3*x3 <= 99)
    model.addConstr(10*x1 + 14*x2 <= 80)
    model.addConstr(x0 + 11*x3 <= 72)
    model.addConstr(x0 + 6*x1 <= 175)
    model.addConstr(6*x1 + 5*x2 <= 150)
    model.addConstr(10*x1 + 14*x3 <= 164)
    model.addConstr(4*x2 + 14*x3 <= 86)
    model.addConstr(2*x0 + 14*x3 <= 130)
    model.addConstr(2*x0 + 10*x1 <= 116)
    model.addConstr(2*x0 + 10*x1 + 4*x2 <= 185)
    model.addConstr(2*x0 + 14*x3 <= 73)
    model.addConstr(2*x0 + 10*x1 <= 40)
    model.addConstr(10*x1 + 14*x3 <= 111)
    model.addConstr(2*x0 + 2*x2 <= 102)
    model.addConstr(2*x0 + 10*x1 + 14*x3 <= 82)

    model.optimize()

    if model.status == gurobi.GRB.Status.OPTIMAL:
        print("Optimal solution found.")
        print("Objective: ", model.objVal)
        print("milligrams of magnesium: ", x0.varValue)
        print("milligrams of vitamin B12: ", x1.varValue)
        print("milligrams of calcium: ", x2.varValue)
        print("milligrams of vitamin B1: ", x3.varValue)
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```