To solve the given optimization problem, we first need to translate the natural language description into a symbolic representation. This involves defining variables for each object (kiwis, fruit salads, apples), formulating the objective function using these variables, and then listing all constraints in terms of these variables.

Let's denote:
- \(x_1\) as the number of kiwis,
- \(x_2\) as the number of fruit salads,
- \(x_3\) as the number of apples.

The objective function to maximize is given by:
\[4x_1^2 + 3x_1x_2 + 4x_1x_3 + 2x_2^2 + 9x_2x_3 + 8x_3^2 + 4x_2 + 6x_3\]

The constraints are:
1. The umami index of kiwis: \(17x_1\)
2. The umami index of fruit salads: \(9x_2\)
3. The umami index of apples: \(11x_3\)
4. The total combined umami index from kiwis and apples must not exceed 236: \(17x_1 + 11x_3 \leq 236\)
5. The total combined umami index from kiwis, fruit salads, and apples must not exceed 242 (but it was corrected to be no more than 236 as per the problem's last constraint which seems to supersede the initial upper bound given for 'r0'): \(17x_1 + 9x_2 + 11x_3 \leq 236\)
6. The number of kiwis does not have to be whole: \(x_1 \in \mathbb{R}\)
7. The number of fruit salads must be a whole number: \(x_2 \in \mathbb{Z}\)
8. The amount of apples must be a whole number: \(x_3 \in \mathbb{Z}\)

Given this, the symbolic representation of the problem is:
```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',
    'x2 >= 0', 
    'x3 >= 0'
  ]
}
```

Now, to solve this problem using Gurobi, we can write the following Python code:

```python
from gurobipy import *

# Create a new model
m = Model("Optimization_Problem")

# Add variables
x1 = m.addVar(vtype=GRB.CONTINUOUS, name="kiwis")
x2 = m.addVar(vtype=GRB.INTEGER, name="fruit_salads")
x3 = m.addVar(vtype=GRB.INTEGER, name="apples")

# Set 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, GRB.MAXIMIZE)

# Add constraints
m.addConstr(17*x1 + 11*x3 <= 236, name="umami_index_kiwis_apples")
m.addConstr(17*x1 + 9*x2 + 11*x3 <= 236, name="total_umami_index")

# Optimize model
m.optimize()

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