## Step 1: Define the symbolic representation of the variables
The variables are 'kiwis', 'fruit salads', and 'apples', which can be represented symbolically as $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Convert the objective function into a symbolic representation
The objective function to maximize is $4x_1^2 + 3x_1x_2 + 4x_1x_3 + 2x_2^2 + 9x_2x_3 + 8x_3^2 + 4x_2 + 6x_3$.

## Step 3: Define the constraints in symbolic notation
The constraints are:
- $17x_1 + 11x_3 \leq 236$
- $17x_1 + 9x_2 + 11x_3 \leq 236$
- $x_1$ is continuous
- $x_2$ is an integer
- $x_3$ is an integer

## 4: Represent the problem in the required JSON format
```json
{
    'sym_variables': [('x1', 'kiwis'), ('x2', 'fruit salads'), ('x3', 'apples')], 
    'objective_function': '4*x1^2 + 3*x1*x2 + 4*x1*x3 + 2*x2^2 + 9*x2*x3 + 8*x3^2 + 4*x2 + 6*x3', 
    'constraints': ['17*x1 + 11*x3 <= 236', '17*x1 + 9*x2 + 11*x3 <= 236', 'x1 >= 0', 'x2 >= 0', 'x3 >= 0']
}
```

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

# Define the model
m = gp.Model()

# Define the variables
x1 = m.addVar(name="kiwis", lb=0, vtype=gp.GRB.CONTINUOUS)
x2 = m.addVar(name="fruit_salads", lb=0, vtype=gp.GRB.INTEGER)
x3 = m.addVar(name="apples", lb=0, vtype=gp.GRB.INTEGER)

# Define the objective function
m.setObjective(4*x1**2 + 3*x1*x2 + 4*x1*x3 + 2*x2**2 + 9*x2*x3 + 8*x3**2 + 4*x2 + 6*x3, gp.GRB.MAXIMIZE)

# Define the constraints
m.addConstr(17*x1 + 11*x3 <= 236)
m.addConstr(17*x1 + 9*x2 + 11*x3 <= 236)

# Solve the model
m.optimize()

# Print the solution
if m.status == gp.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Kiwis: {x1.varValue}")
    print(f"Fruit Salads: {x2.varValue}")
    print(f"Apples: {x3.varValue}")
    print(f"Objective: {m.objVal}")
else:
    print("No optimal solution found.")
```