To solve the given optimization problem, we first need to translate the natural language description into a symbolic representation. This involves defining variables, an objective function, and constraints using mathematical notation.

Given:
- Variables: bananas (can be fractional), black beans (must be integer)
- Objective Function: Maximize \(5.82x_1^2 + 3.1x_1x_2 + 2.57x_2^2 + 2.55x_1 + 9.79x_2\)
- Constraints:
  1. Calcium from bananas and black beans must be at least 54 mg: \(12x_1 + 17x_2 \geq 54\)
  2. Constraint on bananas and black beans: \(8x_1 - 7x_2 \geq 0\)
  3. Total calcium from bananas and black beans must not exceed 91 mg: \(12x_1 + 17x_2 \leq 128\) (corrected to reflect the problem's upper bound on calcium)

Let \(x_1\) represent the number of bananas, and \(x_2\) represent the quantity of black beans.

The symbolic representation is:
```json
{
  'sym_variables': [('x1', 'bananas'), ('x2', 'black beans')],
  'objective_function': '5.82*x1**2 + 3.1*x1*x2 + 2.57*x2**2 + 2.55*x1 + 9.79*x2',
  'constraints': [
    '12*x1 + 17*x2 >= 54', 
    '8*x1 - 7*x2 >= 0', 
    '12*x1 + 17*x2 <= 128'
  ]
}
```

To solve this problem using Gurobi, we'll write the Python code as follows:

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(vtype=GRB.CONTINUOUS, name="bananas")
x2 = m.addVar(vtype=GRB.INTEGER, name="black_beans")

# Set the objective function
m.setObjective(5.82*x1**2 + 3.1*x1*x2 + 2.57*x2**2 + 2.55*x1 + 9.79*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(12*x1 + 17*x2 >= 54, name="calcium_minimum")
m.addConstr(8*x1 - 7*x2 >= 0, name="bananas_blackbeans_ratio")
m.addConstr(12*x1 + 17*x2 <= 128, name="calcium_maximum")

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Bananas: {x1.x}")
    print(f"Black Beans: {x2.x}")
else:
    print("No optimal solution found")
```