## Step 1: Define the symbolic representation of the problem

First, let's define the symbolic representation of the problem. We have the following variables:
- $x_1$ : eggs
- $x_2$ : steaks
- $x_3$ : apple pies
- $x_4$ : kiwis

The objective function to maximize is: $5.3x_1 + 7.7x_2 + 7.28x_3 + 6.33x_4$

## Step 2: List the constraints

Now, let's list the constraints:
- $0.48x_1 + 0.44x_2 + 2.47x_3 + 2.17x_4 \leq 183$ (total iron constraint)
- $0.48x_1 + 2.17x_4 \geq 33$ (eggs and kiwis iron constraint)
- $0.44x_2 + 2.17x_4 \geq 28$ (steaks and kiwis iron constraint)
- $2.47x_3 + 2.17x_4 \geq 31$ (apple pies and kiwis iron constraint)
- $0.44x_2 + 2.47x_3 + 2.17x_4 \geq 33$ (steaks, apple pies, and kiwis iron constraint)
- $0.48x_1 + 0.44x_2 + 2.17x_4 \geq 33$ (eggs, steaks, and kiwis iron constraint)
- $0.48x_1 + 2.47x_3 + 2.17x_4 \geq 33$ (eggs, apple pies, and kiwis iron constraint)
- $0.44x_2 + 2.47x_3 + 2.17x_4 \geq 30$ (steaks, apple pies, and kiwis iron constraint)
- $0.48x_1 + 0.44x_2 + 2.17x_4 \geq 30$ (eggs, steaks, and kiwis iron constraint)
- $0.48x_1 + 2.47x_3 + 2.17x_4 \geq 30$ (eggs, apple pies, and kiwis iron constraint)
- $0.44x_2 + 2.47x_3 + 2.17x_4 \geq 35$ (steaks, apple pies, and kiwis iron constraint)
- $0.48x_1 + 0.44x_2 + 2.17x_4 \geq 35$ (eggs, steaks, and kiwis iron constraint)
- $0.48x_1 + 2.47x_3 + 2.17x_4 \geq 35$ (eggs, apple pies, and kiwis iron constraint)
- $0.48x_1 + 0.44x_2 \leq 148$ (eggs and steaks iron constraint)
- $0.44x_2 + 2.47x_3 \leq 61$ (steaks and apple pies iron constraint)
- $0.44x_2 + 2.17x_4 \leq 95$ (steaks and kiwis iron constraint)
- $0.48x_1 + 2.17x_4 \leq 110$ (eggs and kiwis iron constraint)
- $2.47x_3 + 2.17x_4 \leq 144$ (apple pies and kiwis iron constraint)
- $0.48x_1 + 0.44x_2 + 2.17x_4 \leq 160$ (eggs, steaks, and kiwis iron constraint)
- $0.44x_2 + 2.47x_3 + 2.17x_4 \leq 126$ (steaks, apple pies, and kiwis iron constraint)
- $0.48x_1 + 0.44x_2 + 2.47x_3 + 2.17x_4 \leq 126$ (all variables iron constraint)

## Step 3: Define the symbolic representation in JSON format

Here is the symbolic representation of the problem in JSON format:

```json
{
    'sym_variables': [
        ('x1', 'eggs'), 
        ('x2', 'steaks'), 
        ('x3', 'apple pies'), 
        ('x4', 'kiwis')
    ], 
    'objective_function': '5.3*x1 + 7.7*x2 + 7.28*x3 + 6.33*x4', 
    'constraints': [
        '0.48*x1 + 0.44*x2 + 2.47*x3 + 2.17*x4 <= 183',
        '0.48*x1 + 2.17*x4 >= 33',
        '0.44*x2 + 2.17*x4 >= 28',
        '2.47*x3 + 2.17*x4 >= 31',
        '0.44*x2 + 2.47*x3 + 2.17*x4 >= 33',
        '0.48*x1 + 0.44*x2 + 2.17*x4 >= 33',
        '0.48*x1 + 2.47*x3 + 2.17*x4 >= 33',
        '0.44*x2 + 2.47*x3 + 2.17*x4 >= 30',
        '0.48*x1 + 0.44*x2 + 2.17*x4 >= 30',
        '0.48*x1 + 2.47*x3 + 2.17*x4 >= 30',
        '0.44*x2 + 2.47*x3 + 2.17*x4 >= 35',
        '0.48*x1 + 0.44*x2 + 2.17*x4 >= 35',
        '0.48*x1 + 2.47*x3 + 2.17*x4 >= 35',
        '0.48*x1 + 0.44*x2 <= 148',
        '0.44*x2 + 2.47*x3 <= 61',
        '0.44*x2 + 2.17*x4 <= 95',
        '0.48*x1 + 2.17*x4 <= 110',
        '2.47*x3 + 2.17*x4 <= 144',
        '0.48*x1 + 0.44*x2 + 2.17*x4 <= 160',
        '0.44*x2 + 2.47*x3 + 2.17*x4 <= 126',
        '0.48*x1 + 0.44*x2 + 2.47*x3 + 2.17*x4 <= 126'
    ]
}
```

## Step 4: Write the Gurobi code

Here is the Gurobi code:

```python
import gurobi

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

# Define the variables
eggs = model.addVar(name='eggs', vtype=gurobi.GRB.INTEGER, lb=0)
steaks = model.addVar(name='steaks', vtype=gurobi.GRB.CONTINUOUS, lb=0)
apple_pies = model.addVar(name='apple_pies', vtype=gurobi.GRB.INTEGER, lb=0)
kiwis = model.addVar(name='kiwis', vtype=gurobi.GRB.CONTINUOUS, lb=0)

# Define the objective function
model.setObjective(5.3 * eggs + 7.7 * steaks + 7.28 * apple_pies + 6.33 * kiwis, gurobi.GRB.MAXIMIZE)

# Add constraints
model.addConstr(0.48 * eggs + 0.44 * steaks + 2.47 * apple_pies + 2.17 * kiwis <= 183)
model.addConstr(0.48 * eggs + 2.17 * kiwis >= 33)
model.addConstr(0.44 * steaks + 2.17 * kiwis >= 28)
model.addConstr(2.47 * apple_pies + 2.17 * kiwis >= 31)
model.addConstr(0.44 * steaks + 2.47 * apple_pies + 2.17 * kiwis >= 33)
model.addConstr(0.48 * eggs + 0.44 * steaks + 2.17 * kiwis >= 33)
model.addConstr(0.48 * eggs + 2.47 * apple_pies + 2.17 * kiwis >= 33)
model.addConstr(0.44 * steaks + 2.47 * apple_pies + 2.17 * kiwis >= 30)
model.addConstr(0.48 * eggs + 0.44 * steaks + 2.17 * kiwis >= 30)
model.addConstr(0.48 * eggs + 2.47 * apple_pies + 2.17 * kiwis >= 30)
model.addConstr(0.44 * steaks + 2.47 * apple_pies + 2.17 * kiwis >= 35)
model.addConstr(0.48 * eggs + 0.44 * steaks + 2.17 * kiwis >= 35)
model.addConstr(0.48 * eggs + 2.47 * apple_pies + 2.17 * kiwis >= 35)
model.addConstr(0.48 * eggs + 0.44 * steaks <= 148)
model.addConstr(0.44 * steaks + 2.47 * apple_pies <= 61)
model.addConstr(0.44 * steaks + 2.17 * kiwis <= 95)
model.addConstr(0.48 * eggs + 2.17 * kiwis <= 110)
model.addConstr(2.47 * apple_pies + 2.17 * kiwis <= 144)
model.addConstr(0.48 * eggs + 0.44 * steaks + 2.17 * kiwis <= 160)
model.addConstr(0.44 * steaks + 2.47 * apple_pies + 2.17 * kiwis <= 126)
model.addConstr(0.48 * eggs + 0.44 * steaks + 2.47 * apple_pies + 2.17 * kiwis <= 126)

# Optimize the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print('Objective: ', model.objval)
    print('Eggs: ', eggs.varValue)
    print('Steaks: ', steaks.varValue)
    print('Apple pies: ', apple_pies.varValue)
    print('Kiwis: ', kiwis.varValue)
else:
    print('No optimal solution found')
```