To solve this optimization problem, we first need to translate the natural language description into a symbolic representation. This involves defining variables for each item and then expressing the objective function and constraints using these variables.

Let's define the variables as follows:
- $x_1$ represents the quantity of potatoes.
- $x_2$ represents the number of apple pies.
- $x_3$ represents the quantity of kale salads.

The objective function to maximize is given by: 
\[ 2.08x_1^2 + 2.07x_2^2 + 9.38x_2x_3 \]

The constraints are as follows:
1. Calcium from potatoes, apple pies, and kale salads must not exceed 115 milligrams:
\[ 2.54x_1 + 13.7x_2 + 11.67x_3 \leq 115 \]
2. Calcium from apple pies and kale salads must not exceed 50 milligrams:
\[ 13.7x_2 + 11.67x_3 \leq 50 \]
3. Calcium from potatoes squared and kale salads squared must not exceed 115 milligrams (assuming this refers to the individual contributions, so we have):
\[ 2.54x_1^2 + 11.67x_3^2 \leq 115 \]
4. $x_2$ (apple pies) must be an integer.
5. $x_1$ (potatoes) can be a non-integer value.
6. $x_3$ (kale salads) can also be a non-integer value.

Thus, the symbolic representation of our problem is:
```json
{
    'sym_variables': [('x1', 'quantity of potatoes'), ('x2', 'number of apple pies'), ('x3', 'quantity of kale salads')],
    'objective_function': '2.08*x1**2 + 2.07*x2**2 + 9.38*x2*x3',
    'constraints': [
        '2.54*x1 + 13.7*x2 + 11.67*x3 <= 115',
        '13.7*x2 + 11.67*x3 <= 50',
        '2.54*x1**2 + 11.67*x3**2 <= 115'
    ]
}
```

Now, let's express this problem in Gurobi code:
```python
from gurobipy import *

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

# Define the variables
x1 = m.addVar(lb=0, name="potatoes", vtype=GRB.CONTINUOUS)
x2 = m.addVar(lb=0, name="apple_pies", vtype=GRB.INTEGER)
x3 = m.addVar(lb=0, name="kale_salads", vtype=GRB.CONTINUOUS)

# Set the objective function
m.setObjective(2.08*x1**2 + 2.07*x2**2 + 9.38*x2*x3, GRB.MAXIMIZE)

# Add constraints
m.addConstr(2.54*x1 + 13.7*x2 + 11.67*x3 <= 115, name="total_calcium")
m.addConstr(13.7*x2 + 11.67*x3 <= 50, name="calcium_from_pies_and_salads")
m.addConstr(2.54*x1**2 + 11.67*x3**2 <= 115, name="calcium_from_potatoes_and_salads_squared")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Potatoes: {x1.x}")
    print(f"Apple Pies: {x2.x}")
    print(f"Kale Salads: {x3.x}")
else:
    print("No optimal solution found")
```