## Step 1: Define the symbolic representation of the problem
The problem involves maximizing an objective function subject to several constraints. The variables are 'lemons', 'chicken thighs', 'corn cobs', and 'cheeseburgers', which we can represent symbolically as $x_1, x_2, x_3, x_4$ respectively.

## Step 2: Translate the objective function into symbolic notation
The objective function to maximize is $4.27x_1 + 7.35x_2 + 1.78x_3 + 6.68x_4$.

## 3: List the constraints in symbolic notation
The constraints given can be translated into the following:
- $4.64x_1 \leq 236$
- $4.0x_1 \leq 233$
- $8.84x_2 \leq 236$
- $6.51x_2 \leq 233$
- $9.92x_3 \leq 236$
- $5.58x_3 \leq 233$
- $7.71x_4 \leq 236$
- $1.41x_4 \leq 233$
- $8.84x_2 + 9.92x_3 + 7.71x_4 \geq 32$
- $4.64x_1 + 8.84x_2 + 9.92x_3 \geq 32$
- $4.64x_1 + 8.84x_2 + 7.71x_4 \geq 32$
- $8.84x_2 + 9.92x_3 + 7.71x_4 \geq 52$
- $4.64x_1 + 8.84x_2 + 9.92x_3 \geq 52$
- $4.64x_1 + 8.84x_2 + 7.71x_4 \geq 52$
- $8.84x_2 + 9.92x_3 + 7.71x_4 \geq 47$
- $4.64x_1 + 8.84x_2 + 9.92x_3 \geq 47$
- $4.64x_1 + 8.84x_2 + 7.71x_4 \geq 47$
- $6.51x_2 + 5.58x_3 \geq 49$
- $4.0x_1 + 6.51x_2 + 1.41x_4 \geq 43$
- $6.51x_2 + 5.58x_3 + 1.41x_4 \geq 43$
- $4.0x_1 + 6.51x_2 + 1.41x_4 \geq 47$
- $6.51x_2 + 5.58x_3 + 1.41x_4 \geq 47$
- $9.92x_3 + 7.71x_4 \leq 196$
- $4.64x_1 + 7.71x_4 \leq 190$
- $4.64x_1 + 8.84x_2 + 7.71x_4 \leq 236$
- $4.64x_1 + 8.84x_2 + 9.92x_3 \leq 204$
- $4.64x_1 + 8.84x_2 + 9.92x_3 + 7.71x_4 \leq 204$
- $4.0x_1 + 5.58x_3 \leq 60$
- $4.0x_1 + 6.51x_2 \leq 69$
- $4.0x_1 + 1.41x_4 \leq 163$
- $4.0x_1 + 6.51x_2 + 5.58x_3 \leq 94$
- $4.0x_1 + 5.58x_3 + 1.41x_4 \leq 136$
- $6.51x_2 + 5.58x_3 + 1.41x_4 \leq 169$
- $4.0x_1 + 6.51x_2 + 1.41x_4 \leq 211$
- $4.0x_1 + 6.51x_2 + 5.58x_3 + 1.41x_4 \leq 211$
- $x_1$ is an integer
- $x_2$ is an integer
- $x_3$ is continuous
- $x_4$ is continuous

## 4: Provide the symbolic representation in the required format
```json
{
    'sym_variables': [
        ('x1', 'lemons'), 
        ('x2', 'chicken thighs'), 
        ('x3', 'corn cobs'), 
        ('x4', 'cheeseburgers')
    ], 
    'objective_function': '4.27*x1 + 7.35*x2 + 1.78*x3 + 6.68*x4', 
    'constraints': [
        '4.64*x1 <= 236',
        '4.0*x1 <= 233',
        '8.84*x2 <= 236',
        '6.51*x2 <= 233',
        '9.92*x3 <= 236',
        '5.58*x3 <= 233',
        '7.71*x4 <= 236',
        '1.41*x4 <= 233',
        '8.84*x2 + 9.92*x3 + 7.71*x4 >= 32',
        '4.64*x1 + 8.84*x2 + 9.92*x3 >= 32',
        '4.64*x1 + 8.84*x2 + 7.71*x4 >= 32',
        '8.84*x2 + 9.92*x3 + 7.71*x4 >= 52',
        '4.64*x1 + 8.84*x2 + 9.92*x3 >= 52',
        '4.64*x1 + 8.84*x2 + 7.71*x4 >= 52',
        '8.84*x2 + 9.92*x3 + 7.71*x4 >= 47',
        '4.64*x1 + 8.84*x2 + 9.92*x3 >= 47',
        '4.64*x1 + 8.84*x2 + 7.71*x4 >= 47',
        '6.51*x2 + 5.58*x3 >= 49',
        '4.0*x1 + 6.51*x2 + 1.41*x4 >= 43',
        '6.51*x2 + 5.58*x3 + 1.41*x4 >= 43',
        '4.0*x1 + 6.51*x2 + 1.41*x4 >= 47',
        '6.51*x2 + 5.58*x3 + 1.41*x4 >= 47',
        '9.92*x3 + 7.71*x4 <= 196',
        '4.64*x1 + 7.71*x4 <= 190',
        '4.64*x1 + 8.84*x2 + 7.71*x4 <= 236',
        '4.64*x1 + 8.84*x2 + 9.92*x3 <= 204',
        '4.64*x1 + 8.84*x2 + 9.92*x3 + 7.71*x4 <= 204',
        '4.0*x1 + 5.58*x3 <= 60',
        '4.0*x1 + 6.51*x2 <= 69',
        '4.0*x1 + 1.41*x4 <= 163',
        '4.0*x1 + 6.51*x2 + 5.58*x3 <= 94',
        '4.0*x1 + 5.58*x3 + 1.41*x4 <= 136',
        '6.51*x2 + 5.58*x3 + 1.41*x4 <= 169',
        '4.0*x1 + 6.51*x2 + 1.41*x4 <= 211',
        '4.0*x1 + 6.51*x2 + 5.58*x3 + 1.41*x4 <= 211'
    ]
}
```

## 5: Implement the optimization problem using Gurobi
```python
import gurobi

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

    # Define variables
    x1 = model.addVar(name="lemons", vtype=gurobi.GRB.INTEGER)
    x2 = model.addVar(name="chicken thighs", vtype=gurobi.GRB.INTEGER)
    x3 = model.addVar(name="corn cobs")
    x4 = model.addVar(name="cheeseburgers")

    # Objective function
    model.setObjective(4.27 * x1 + 7.35 * x2 + 1.78 * x3 + 6.68 * x4, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(4.64 * x1 <= 236)
    model.addConstr(4.0 * x1 <= 233)
    model.addConstr(8.84 * x2 <= 236)
    model.addConstr(6.51 * x2 <= 233)
    model.addConstr(9.92 * x3 <= 236)
    model.addConstr(5.58 * x3 <= 233)
    model.addConstr(7.71 * x4 <= 236)
    model.addConstr(1.41 * x4 <= 233)

    model.addConstr(8.84 * x2 + 9.92 * x3 + 7.71 * x4 >= 32)
    model.addConstr(4.64 * x1 + 8.84 * x2 + 9.92 * x3 >= 32)
    model.addConstr(4.64 * x1 + 8.84 * x2 + 7.71 * x4 >= 32)
    model.addConstr(8.84 * x2 + 9.92 * x3 + 7.71 * x4 >= 52)
    model.addConstr(4.64 * x1 + 8.84 * x2 + 9.92 * x3 >= 52)
    model.addConstr(4.64 * x1 + 8.84 * x2 + 7.71 * x4 >= 52)
    model.addConstr(8.84 * x2 + 9.92 * x3 + 7.71 * x4 >= 47)
    model.addConstr(4.64 * x1 + 8.84 * x2 + 9.92 * x3 >= 47)
    model.addConstr(4.64 * x1 + 8.84 * x2 + 7.71 * x4 >= 47)

    model.addConstr(6.51 * x2 + 5.58 * x3 >= 49)
    model.addConstr(4.0 * x1 + 6.51 * x2 + 1.41 * x4 >= 43)
    model.addConstr(6.51 * x2 + 5.58 * x3 + 1.41 * x4 >= 43)
    model.addConstr(4.0 * x1 + 6.51 * x2 + 1.41 * x4 >= 47)
    model.addConstr(6.51 * x2 + 5.58 * x3 + 1.41 * x4 >= 47)

    model.addConstr(9.92 * x3 + 7.71 * x4 <= 196)
    model.addConstr(4.64 * x1 + 7.71 * x4 <= 190)
    model.addConstr(4.64 * x1 + 8.84 * x2 + 7.71 * x4 <= 236)
    model.addConstr(4.64 * x1 + 8.84 * x2 + 9.92 * x3 <= 204)
    model.addConstr(4.64 * x1 + 8.84 * x2 + 9.92 * x3 + 7.71 * x4 <= 204)

    model.addConstr(4.0 * x1 + 5.58 * x3 <= 60)
    model.addConstr(4.0 * x1 + 6.51 * x2 <= 69)
    model.addConstr(4.0 * x1 + 1.41 * x4 <= 163)
    model.addConstr(4.0 * x1 + 6.51 * x2 + 5.58 * x3 <= 94)
    model.addConstr(4.0 * x1 + 5.58 * x3 + 1.41 * x4 <= 136)
    model.addConstr(6.51 * x2 + 5.58 * x3 + 1.41 * x4 <= 169)
    model.addConstr(4.0 * x1 + 6.51 * x2 + 1.41 * x4 <= 211)
    model.addConstr(4.0 * x1 + 6.51 * x2 + 5.58 * x3 + 1.41 * x4 <= 211)

    model.optimize()

    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objval)
        print("Lemon: ", x1.varValue)
        print("Chicken thighs: ", x2.varValue)
        print("Corn cobs: ", x3.varValue)
        print("Cheeseburgers: ", x4.varValue)
    else:
        print("No optimal solution found")

optimize_problem()
```