## Step 1: Define the symbolic representation of the problem
The problem involves four variables: ['milligrams of vitamin E', 'milligrams of iron', 'milligrams of vitamin A', 'milligrams of vitamin B9']. Let's denote them as $x_1$, $x_2$, $x_3$, and $x_4$ respectively.

## Step 2: Convert the objective function into symbolic notation
The objective function to minimize is $8x_1 + 4x_2 + 2x_3 + 9x_4$.

## Step 3: List the constraints in symbolic notation
The constraints given are:
- $0.37x_2 + 11.4x_4 \geq 26$
- $10.77x_1 + 0.37x_2 \geq 25$
- $10.77x_1 + 11.4x_4 \geq 33$
- $10.77x_1 + 0.37x_2 + 9.84x_3 \geq 36$
- $10.77x_1 + 0.37x_2 + 9.84x_3 + 11.4x_4 \geq 36$
- $1.19x_2 + 2.39x_4 \geq 92$
- $1.19x_2 + 9.85x_3 \geq 39$
- $11.54x_1 + 9.85x_3 \geq 56$
- $11.54x_1 + 1.19x_2 + 9.85x_3 + 2.39x_4 \geq 56$
- $6.46x_2 + 6.74x_3 \geq 50$
- $8.5x_1 + 6.74x_3 \geq 43$
- $6.46x_2 + 9.65x_4 \geq 65$
- $8.5x_1 + 6.46x_2 + 6.74x_3 + 9.65x_4 \geq 65$
- $10x_2 - x_4 \geq 0$
- $2x_3 - 9x_4 \geq 0$
- $6.46x_2 + 6.74x_3 \leq 215$
- $6.74x_3 + 9.65x_4 \leq 80$
- $6.46x_2 + 9.65x_4 \leq 282$
- $8.5x_1 + 6.46x_2 \leq 178$
- $8.5x_1 + 9.65x_4 \leq 223$
- $x_2$ is integer
- $x_4$ is integer

## 4: Define the symbolic variables and constraints for the output
The symbolic representation is:
```json
{
    'sym_variables': [
        ('x1', 'milligrams of vitamin E'), 
        ('x2', 'milligrams of iron'), 
        ('x3', 'milligrams of vitamin A'), 
        ('x4', 'milligrams of vitamin B9')
    ], 
    'objective_function': '8*x1 + 4*x2 + 2*x3 + 9*x4', 
    'constraints': [
        '0.37*x2 + 11.4*x4 >= 26',
        '10.77*x1 + 0.37*x2 >= 25',
        '10.77*x1 + 11.4*x4 >= 33',
        '10.77*x1 + 0.37*x2 + 9.84*x3 >= 36',
        '10.77*x1 + 0.37*x2 + 9.84*x3 + 11.4*x4 >= 36',
        '1.19*x2 + 2.39*x4 >= 92',
        '1.19*x2 + 9.85*x3 >= 39',
        '11.54*x1 + 9.85*x3 >= 56',
        '11.54*x1 + 1.19*x2 + 9.85*x3 + 2.39*x4 >= 56',
        '6.46*x2 + 6.74*x3 >= 50',
        '8.5*x1 + 6.74*x3 >= 43',
        '6.46*x2 + 9.65*x4 >= 65',
        '8.5*x1 + 6.46*x2 + 6.74*x3 + 9.65*x4 >= 65',
        '10*x2 - x4 >= 0',
        '2*x3 - 9*x4 >= 0',
        '6.46*x2 + 6.74*x3 <= 215',
        '6.74*x3 + 9.65*x4 <= 80',
        '6.46*x2 + 9.65*x4 <= 282',
        '8.5*x1 + 6.46*x2 <= 178',
        '8.5*x1 + 9.65*x4 <= 223'
    ]
}
```

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

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

    # Define variables
    x1 = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="x1")  # milligrams of vitamin E
    x2 = model.addVar(lb=0, ub=gurobi.GRB.INFINITY, type=gurobi.GRB.INTEGER, name="x2")  # milligrams of iron
    x3 = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="x3")  # milligrams of vitamin A
    x4 = model.addVar(lb=0, ub=gurobi.GRB.INFINITY, type=gurobi.GRB.INTEGER, name="x4")  # milligrams of vitamin B9

    # Objective function
    model.setObjective(8 * x1 + 4 * x2 + 2 * x3 + 9 * x4, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(0.37 * x2 + 11.4 * x4 >= 26)
    model.addConstr(10.77 * x1 + 0.37 * x2 >= 25)
    model.addConstr(10.77 * x1 + 11.4 * x4 >= 33)
    model.addConstr(10.77 * x1 + 0.37 * x2 + 9.84 * x3 >= 36)
    model.addConstr(10.77 * x1 + 0.37 * x2 + 9.84 * x3 + 11.4 * x4 >= 36)
    model.addConstr(1.19 * x2 + 2.39 * x4 >= 92)
    model.addConstr(1.19 * x2 + 9.85 * x3 >= 39)
    model.addConstr(11.54 * x1 + 9.85 * x3 >= 56)
    model.addConstr(11.54 * x1 + 1.19 * x2 + 9.85 * x3 + 2.39 * x4 >= 56)
    model.addConstr(6.46 * x2 + 6.74 * x3 >= 50)
    model.addConstr(8.5 * x1 + 6.74 * x3 >= 43)
    model.addConstr(6.46 * x2 + 9.65 * x4 >= 65)
    model.addConstr(8.5 * x1 + 6.46 * x2 + 6.74 * x3 + 9.65 * x4 >= 65)
    model.addConstr(10 * x2 - x4 >= 0)
    model.addConstr(2 * x3 - 9 * x4 >= 0)
    model.addConstr(6.46 * x2 + 6.74 * x3 <= 215)
    model.addConstr(6.74 * x3 + 9.65 * x4 <= 80)
    model.addConstr(6.46 * x2 + 9.65 * x4 <= 282)
    model.addConstr(8.5 * x1 + 6.46 * x2 <= 178)
    model.addConstr(8.5 * x1 + 9.65 * x4 <= 223)

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"x1: {x1.varValue}")
        print(f"x2: {x2.varValue}")
        print(f"x3: {x3.varValue}")
        print(f"x4: {x4.varValue}")
        print(f"Objective: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```