## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'knishes' and 'kiwis', which can be represented symbolically as $x_1$ and $x_2$ respectively. The objective function to maximize is $4x_1 + 8x_2$. The constraints are:
- $22x_1 + 28x_2 \geq 21$ (at least 21 grams of carbohydrates)
- $14x_1 + 10x_2 \geq 40$ (at least 40 milligrams of calcium)
- $9x_1 - 3x_2 \geq 0$ (9 times knishes minus 3 times kiwis is at least zero)
- $22x_1 + 28x_2 \leq 49$ (at most 49 grams of carbohydrates from knishes and kiwis, corrected to reflect that both have the same upper bound for carbs)
- $14x_1 + 10x_2 \leq 77$ (at most 77 milligrams of calcium from knishes and kiwis)
- $x_1$ is an integer (knishes must be an integer number)
- $x_2$ can be a non-integer (kiwis can be a non-integer number)

## 2: Correct and Clarify Constraints
Upon review, there seems to be a repetition in the carbohydrate constraint. The problem states:
- You need to get at least 21 grams of carbohydrates.
- You must get at most 49 grams of carbohydrates.

And similarly for calcium:
- You must get no more than 77 milligrams of calcium.

These will be directly translated into the model.

## 3: Formulate the Symbolic Representation
The symbolic representation is:
```json
{
'sym_variables': [('x1', 'knishes'), ('x2', 'kiwis')],
'objective_function': '4*x1 + 8*x2',
'constraints': [
    '22*x1 + 28*x2 >= 21',
    '14*x1 + 10*x2 >= 40',
    '9*x1 - 3*x2 >= 0',
    '22*x1 + 28*x2 <= 49',
    '14*x1 + 10*x2 <= 77'
]
}
```

## 4: Implement the Gurobi Model
Now, let's implement this in Gurobi:
```python
import gurobi

def solve_optimization_problem():
    # Create a new model
    model = gurobi.Model()

    # Define variables
    knishes = model.addVar(name="knishes", vtype=gurobi.GRB.INTEGER)
    kiwis = model.addVar(name="kiwis")

    # Objective function: Maximize 4*knishes + 8*kiwis
    model.setObjective(4 * knishes + 8 * kiwis, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(22 * knishes + 28 * kiwis >= 21, name="carb_min")
    model.addConstr(14 * knishes + 10 * kiwis >= 40, name="calcium_min")
    model.addConstr(9 * knishes - 3 * kiwis >= 0, name="knish_kiwi_ratio")
    model.addConstr(22 * knishes + 28 * kiwis <= 49, name="carb_max")
    model.addConstr(14 * knishes + 10 * kiwis <= 77, name="calcium_max")

    # Optimize
    model.optimize()

    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Knishes: {knishes.varValue}")
        print(f"Kiwis: {kiwis.varValue}")
        print(f"Objective: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```