## Step 1: Define the symbolic representation of the variables
The variables are defined as follows: 
- $x_1$ represents 'grams of carbohydrates'
- $x_2$ represents 'milligrams of potassium'
- $x_3$ represents 'milligrams of vitamin B9'
- $x_4$ represents 'milligrams of vitamin B1'
- $x_5$ represents 'milligrams of vitamin E'

## Step 2: Convert the objective function into a symbolic representation
The objective function to minimize is $1.42x_1 + 9.1x_2 + 8.22x_3 + 3.08x_4 + 2.28x_5$.

## 3: Define the constraints in symbolic notation
The constraints are:
- $7x_1 \leq 221$
- $7x_2 \leq 221$
- $9x_3 \leq 221$
- $6x_4 \leq 221$
- $7x_5 \leq 221$
- $9x_3 + 6x_4 \geq 40$
- $7x_2 + 6x_4 \geq 16$
- $7x_1 + 9x_3 \geq 33$
- $7x_1 + 7x_2 + 7x_5 \geq 41$
- $7x_1 + 7x_2 + 9x_3 + 6x_4 + 7x_5 \geq 41$
- $6x_3 - 6x_4 \geq 0$
- $5x_3 - 4x_5 \geq 0$
- $-3x_1 + 5x_2 \geq 0$
- $7x_2 + 9x_3 \leq 54$
- $7x_2 + 6x_4 \leq 56$
- $7x_1 + 6x_4 \leq 169$
- $7x_2 + 9x_3 + 6x_4 \leq 125$
- $7x_1 + 9x_3 + 6x_4 \leq 205$
- $7x_1 + 9x_3 + 7x_5 \leq 122$
- $9x_3 + 6x_4 + 7x_5 \leq 215$
- $7x_1 + 7x_2 + 6x_4 \leq 111$
- $7x_1 + 7x_2 + 7x_5 \leq 158$
- $7x_2 + 6x_4 + 7x_5 \leq 58$
- $7x_2 + 9x_3 + 7x_5 \leq 197$

## 4: Create the Gurobi model and variables
We will now create a Gurobi model and define the variables.

## 5: Implement the objective function and constraints in Gurobi
```python
import gurobi

# Create a new Gurobi model
model = gurobi.Model()

# Define the variables
x1 = model.addVar(name="x1", lb=0)  # grams of carbohydrates
x2 = model.addVar(name="x2", lb=0)  # milligrams of potassium
x3 = model.addVar(name="x3", lb=0)  # milligrams of vitamin B9
x4 = model.addVar(name="x4", lb=0)  # milligrams of vitamin B1
x5 = model.addVar(name="x5", lb=0)  # milligrams of vitamin E

# Objective function
model.setObjective(1.42 * x1 + 9.1 * x2 + 8.22 * x3 + 3.08 * x4 + 2.28 * x5, gurobi.GRB.MINIMIZE)

# Constraints
model.addConstr(7 * x1 <= 221)
model.addConstr(7 * x2 <= 221)
model.addConstr(9 * x3 <= 221)
model.addConstr(6 * x4 <= 221)
model.addConstr(7 * x5 <= 221)

model.addConstr(9 * x3 + 6 * x4 >= 40)
model.addConstr(7 * x2 + 6 * x4 >= 16)
model.addConstr(7 * x1 + 9 * x3 >= 33)
model.addConstr(7 * x1 + 7 * x2 + 7 * x5 >= 41)
model.addConstr(7 * x1 + 7 * x2 + 9 * x3 + 6 * x4 + 7 * x5 >= 41)

model.addConstr(6 * x3 - 6 * x4 >= 0)
model.addConstr(5 * x3 - 4 * x5 >= 0)
model.addConstr(-3 * x1 + 5 * x2 >= 0)

model.addConstr(7 * x2 + 9 * x3 <= 54)
model.addConstr(7 * x2 + 6 * x4 <= 56)
model.addConstr(7 * x1 + 6 * x4 <= 169)
model.addConstr(7 * x2 + 9 * x3 + 6 * x4 <= 125)
model.addConstr(7 * x1 + 9 * x3 + 6 * x4 <= 205)
model.addConstr(7 * x1 + 9 * x3 + 7 * x5 <= 122)
model.addConstr(9 * x3 + 6 * x4 + 7 * x5 <= 215)
model.addConstr(7 * x1 + 7 * x2 + 6 * x4 <= 111)
model.addConstr(7 * x1 + 7 * x2 + 7 * x5 <= 158)
model.addConstr(7 * x2 + 6 * x4 + 7 * x5 <= 58)
model.addConstr(7 * x2 + 9 * x3 + 7 * x5 <= 197)

# Optimize the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print("Objective: ", model.objVal)
    print("x1: ", x1.varValue)
    print("x2: ", x2.varValue)
    print("x3: ", x3.varValue)
    print("x4: ", x4.varValue)
    print("x5: ", x5.varValue)
else:
    print("The model is infeasible")
```

```json
{
    'sym_variables': [
        ('x1', 'grams of carbohydrates'), 
        ('x2', 'milligrams of potassium'), 
        ('x3', 'milligrams of vitamin B9'), 
        ('x4', 'milligrams of vitamin B1'), 
        ('x5', 'milligrams of vitamin E')
    ], 
    'objective_function': '1.42*x1 + 9.1*x2 + 8.22*x3 + 3.08*x4 + 2.28*x5', 
    'constraints': [
        '7*x1 <= 221', 
        '7*x2 <= 221', 
        '9*x3 <= 221', 
        '6*x4 <= 221', 
        '7*x5 <= 221', 
        '9*x3 + 6*x4 >= 40', 
        '7*x2 + 6*x4 >= 16', 
        '7*x1 + 9*x3 >= 33', 
        '7*x1 + 7*x2 + 7*x5 >= 41', 
        '7*x1 + 7*x2 + 9*x3 + 6*x4 + 7*x5 >= 41', 
        '6*x3 - 6*x4 >= 0', 
        '5*x3 - 4*x5 >= 0', 
        '-3*x1 + 5*x2 >= 0', 
        '7*x2 + 9*x3 <= 54', 
        '7*x2 + 6*x4 <= 56', 
        '7*x1 + 6*x4 <= 169', 
        '7*x2 + 9*x3 + 6*x4 <= 125', 
        '7*x1 + 9*x3 + 6*x4 <= 205', 
        '7*x1 + 9*x3 + 7*x5 <= 122', 
        '9*x3 + 6*x4 + 7*x5 <= 215', 
        '7*x1 + 7*x2 + 6*x4 <= 111', 
        '7*x1 + 7*x2 + 7*x5 <= 158', 
        '7*x2 + 6*x4 + 7*x5 <= 58', 
        '7*x2 + 9*x3 + 7*x5 <= 197'
    ]
}
```