## Step 1: Define the symbolic representation of the variables
The variables are ['sashimi', 'kale salads', 'corn cobs', 'steaks', 'kiwis'] which can be represented symbolically as ['x1', 'x2', 'x3', 'x4', 'x5'].

## Step 2: Create a dictionary mapping symbolic variables to their natural language counterparts
The symbolic variables and their corresponding natural language objects are:
- x1: sashimi
- x2: kale salads
- x3: corn cobs
- x4: steaks
- x5: kiwis

## Step 3: Formulate the objective function using symbolic variables
The objective function to maximize is: $8x_1 + 5x_2 + 8x_3 + 9x_4 + 5x_5$.

## 4: Define the constraints
The constraints given are:
- $26x_2 + 5x_4 + x_5 \geq 32$ (at least 32 milligrams of calcium from kale salads, steaks, and kiwis)
- $2x_3 + 5x_4 \leq 81$ (at most 81 milligrams of calcium from corn cobs and steaks)
- $17x_1 + x_5 \leq 48$ (at most 48 milligrams of calcium from sashimi and kiwis)
- $17x_1 + 2x_3 + x_5 \leq 110$ (at most 110 milligrams of calcium from sashimi, corn cobs, and kiwis)
- $17x_1 + 26x_2 + 2x_3 + 5x_4 + x_5 \leq 110$ (at most 110 milligrams of calcium from all sources)
- $x_1, x_2, x_3, x_4, x_5 \geq 0$ (non-negativity constraints, allowing for fractional amounts)

## 5: Convert the problem into a Gurobi model
We will use Gurobi to solve this linear programming problem.

## 6: Write the symbolic representation of the problem
```json
{
    'sym_variables': [('x1', 'sashimi'), ('x2', 'kale salads'), ('x3', 'corn cobs'), ('x4', 'steaks'), ('x5', 'kiwis')],
    'objective_function': '8*x1 + 5*x2 + 8*x3 + 9*x4 + 5*x5',
    'constraints': [
        '26*x2 + 5*x4 + x5 >= 32',
        '2*x3 + 5*x4 <= 81',
        '17*x1 + x5 <= 48',
        '17*x1 + 2*x3 + x5 <= 110',
        '17*x1 + 26*x2 + 2*x3 + 5*x4 + x5 <= 110'
    ]
}
```

## 7: Implement the problem in Gurobi
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name='sashimi', lb=0)
    x2 = model.addVar(name='kale_salads', lb=0)
    x3 = model.addVar(name='corn_cobs', lb=0)
    x4 = model.addVar(name='steaks', lb=0)
    x5 = model.addVar(name='kiwis', lb=0)

    # Define the objective function
    model.setObjective(8*x1 + 5*x2 + 8*x3 + 9*x4 + 5*x5, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(26*x2 + 5*x4 + x5 >= 32)
    model.addConstr(2*x3 + 5*x4 <= 81)
    model.addConstr(17*x1 + x5 <= 48)
    model.addConstr(17*x1 + 2*x3 + x5 <= 110)
    model.addConstr(17*x1 + 26*x2 + 2*x3 + 5*x4 + x5 <= 110)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print('Objective: ', model.objVal)
        print('sashimi: ', x1.varValue)
        print('kale salads: ', x2.varValue)
        print('corn cobs: ', x3.varValue)
        print('steaks: ', x4.varValue)
        print('kiwis: ', x5.varValue)
    else:
        print('The model is infeasible')

solve_optimization_problem()
```