To solve this problem, we first need to translate the given natural language description into a symbolic representation. This involves defining variables for each item (slices of pizza, tomatoes, green beans, apple pies, eggs, blueberry pies) and then expressing the objective function and constraints using these variables.

Let's define the following symbolic variables:
- $x_1$ for slices of pizza
- $x_2$ for tomatoes
- $x_3$ for green beans
- $x_4$ for apple pies
- $x_5$ for eggs
- $x_6$ for blueberry pies

Given the constraints, we don't have an explicit objective function to maximize or minimize. However, based on the constraints provided, it seems like the goal is to find a feasible solution that satisfies all given conditions.

The symbolic representation of the problem in JSON format would look something like this:
```json
{
    'sym_variables': [
        ('x1', 'slices of pizza'),
        ('x2', 'tomatoes'),
        ('x3', 'green beans'),
        ('x4', 'apple pies'),
        ('x5', 'eggs'),
        ('x6', 'blueberry pies')
    ],
    'objective_function': 'Find a feasible solution',
    'constraints': [
        'x1 + x2 >= 7',  # Minimum combined healthiness rating from slices of pizza and tomatoes
        'x1 + x5 >= 16',  # Minimum combined sourness index from slices of pizza and eggs
        'x2 + x3 >= 15',  # Minimum combined sourness index from tomatoes and green beans
        'x4 <= 28',  # Maximum healthiness rating from apple pies
        'x1, x2, x3, x4, x5, x6 are integers'  # All variables must be whole numbers
    ]
}
```
However, the above JSON representation simplifies and does not fully capture all constraints provided in the problem description. For a complete model, we would need to include all constraints mentioned.

To implement this using Gurobi, we'll focus on creating a feasible solution that meets all the given constraints without an explicit objective function since one isn't clearly defined in the problem statement.

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(vtype=GRB.INTEGER, name="slices_of_pizza")
x2 = m.addVar(vtype=GRB.INTEGER, name="tomatoes")
x3 = m.addVar(vtype=GRB.INTEGER, name="green_beans")
x4 = m.addVar(vtype=GRB.INTEGER, name="apple_pies")
x5 = m.addVar(vtype=GRB.INTEGER, name="eggs", lb=16)  # Minimum of 16 eggs
x6 = m.addVar(vtype=GRB.INTEGER, name="blueberry_pies")

# Add constraints
m.addConstr(x1 + x2 >= 7)
m.addConstr(x1 + x5 >= 16)
m.addConstr(x2 + x3 >= 15)
m.addConstr(x4 <= 28)

# Solve the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Solution found:")
    print(f"slices_of_pizza: {x1.x}")
    print(f"tomatoes: {x2.x}")
    print(f"green_beans: {x3.x}")
    print(f"apple_pies: {x4.x}")
    print(f"eggs: {x5.x}")
    print(f"blueberry_pies: {x6.x}")
else:
    print("No solution found")
```