To solve the given optimization problem, we first need to translate the natural language description into a symbolic representation. This involves defining variables for each item (slices of pizza, kale salads, and green beans), formulating the objective function using these variables, and then listing all constraints as semi-algebraic expressions.

Let's denote:
- \(x_1\) as the quantity of slices of pizza,
- \(x_2\) as the total number of kale salads,
- \(x_3\) as the number of green beans.

The objective function to minimize is: \(5.89x_1 + 2.73x_2 + 3.28x_3\).

Constraints:
1. \(14.48x_1 + 11.82x_2 \geq 29\) (at least 29 grams of fat from slices of pizza and kale salads),
2. \(14.48x_1 + 11.82x_2 + 15.47x_3 \geq 29\) (at least 29 grams of fat from all items, noting this constraint seems redundant given the first but will be included as per the problem statement),
3. \(10x_1 - 10x_3 \geq 0\) (constraint on slices of pizza and green beans),
4. \(7x_1 - 5x_2 \geq 0\) (constraint on slices of pizza and kale salads).

Given that \(x_1\) must be a non-fractional amount (integer), \(x_2\) also an integer, but \(x_3\) can be fractional, we represent these conditions as:
- \(x_1 \in \mathbb{Z}\),
- \(x_2 \in \mathbb{Z}\),
- \(x_3 \in \mathbb{R}\).

Thus, the symbolic representation of our problem is:
```json
{
  'sym_variables': [('x1', 'slices of pizza'), ('x2', 'kale salads'), ('x3', 'green beans')],
  'objective_function': '5.89*x1 + 2.73*x2 + 3.28*x3',
  'constraints': [
    '14.48*x1 + 11.82*x2 >= 29',
    '14.48*x1 + 11.82*x2 + 15.47*x3 >= 29',
    '10*x1 - 10*x3 >= 0',
    '7*x1 - 5*x2 >= 0'
  ]
}
```

Now, let's implement this problem using Gurobi in Python:

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(vtype=GRB.INTEGER, name="slices_of_pizza")
x2 = m.addVar(vtype=GRB.INTEGER, name="kale_salads")
x3 = m.addVar(vtype=GRB.CONTINUOUS, name="green_beans")

# Set the objective function
m.setObjective(5.89*x1 + 2.73*x2 + 3.28*x3, GRB.MINIMIZE)

# Add constraints
m.addConstr(14.48*x1 + 11.82*x2 >= 29, name="fat_from_pizza_and_salads")
m.addConstr(14.48*x1 + 11.82*x2 + 15.47*x3 >= 29, name="total_fat")
m.addConstr(10*x1 - 10*x3 >= 0, name="pizza_vs_green_beans")
m.addConstr(7*x1 - 5*x2 >= 0, name="pizza_vs_salads")

# Optimize the model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"slices_of_pizza: {x1.x}")
    print(f"kale_salads: {x2.x}")
    print(f"green_beans: {x3.x}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found")
```