To solve the optimization problem described, we first need to convert the natural language description into a symbolic representation. This involves defining variables for each item (chicken breasts, oreos, corn cobs, and knishes), formulating the objective function using these variables, and translating the constraints into mathematical expressions.

Let's denote:
- \(x_1\) as the quantity of chicken breasts,
- \(x_2\) as the quantity of oreos,
- \(x_3\) as the quantity of corn cobs,
- \(x_4\) as the quantity of knishes.

The objective function to maximize is: \(7x_1 + 9x_2 + 5x_3 + 2x_4\).

Given constraints:
1. Carbohydrates from chicken breasts plus oreos: \(14.86x_1 + 9.17x_2 \geq 19\)
2. Carbohydrates from oreos plus corn cobs: \(9.17x_2 + 16.5x_3 \geq 15\)
3. Carbohydrates from chicken breasts plus knishes: \(14.86x_1 + 0.36x_4 \leq 30\)
4. Carbohydrates from oreos and knishes: \(9.17x_2 + 0.36x_4 \leq 40\)
5. Carbohydrates from corn cobs and knishes: \(16.5x_3 + 0.36x_4 \leq 64\)
6. Carbohydrates from chicken breasts plus oreos: \(14.86x_1 + 9.17x_2 \leq 51\)
7. Total carbohydrates from all items: \(14.86x_1 + 9.17x_2 + 16.5x_3 + 0.36x_4 \leq 88\)

The symbolic representation of the problem is:
```json
{
    'sym_variables': [('x1', 'chicken breasts'), ('x2', 'oreos'), ('x3', 'corn cobs'), ('x4', 'knishes')],
    'objective_function': '7*x1 + 9*x2 + 5*x3 + 2*x4',
    'constraints': [
        '14.86*x1 + 9.17*x2 >= 19',
        '9.17*x2 + 16.5*x3 >= 15',
        '14.86*x1 + 0.36*x4 <= 30',
        '9.17*x2 + 0.36*x4 <= 40',
        '16.5*x3 + 0.36*x4 <= 64',
        '14.86*x1 + 9.17*x2 <= 51',
        '14.86*x1 + 9.17*x2 + 16.5*x3 + 0.36*x4 <= 88'
    ]
}
```

Now, let's implement this optimization problem using Gurobi in Python:
```python
from gurobipy import *

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

# Define variables
x1 = model.addVar(vtype=GRB.INTEGER, name="chicken_breasts")
x2 = model.addVar(vtype=GRB.INTEGER, name="oreos")
x3 = model.addVar(vtype=GRB.INTEGER, name="corn_cobs")
x4 = model.addVar(vtype=GRB.CONTINUOUS, name="knishes")

# Set the objective function
model.setObjective(7*x1 + 9*x2 + 5*x3 + 2*x4, GRB.MAXIMIZE)

# Add constraints
model.addConstr(14.86*x1 + 9.17*x2 >= 19, "carb_chicken_oreos_min")
model.addConstr(9.17*x2 + 16.5*x3 >= 15, "carb_oreos_corn_min")
model.addConstr(14.86*x1 + 0.36*x4 <= 30, "carb_chicken_knishes_max")
model.addConstr(9.17*x2 + 0.36*x4 <= 40, "carb_oreos_knishes_max")
model.addConstr(16.5*x3 + 0.36*x4 <= 64, "carb_corn_knishes_max")
model.addConstr(14.86*x1 + 9.17*x2 <= 51, "carb_chicken_oreos_max")
model.addConstr(14.86*x1 + 9.17*x2 + 16.5*x3 + 0.36*x4 <= 88, "total_carb_max")

# Optimize the model
model.optimize()

# Print the solution
if model.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Chicken Breasts: {x1.x}")
    print(f"Oreos: {x2.x}")
    print(f"Corn Cobs: {x3.x}")
    print(f"Knishes: {x4.x}")
    print(f"Objective Function Value: {model.objVal}")
else:
    print("No optimal solution found")
```