To tackle the given optimization problem, we first need to break down the natural language description into a symbolic representation. This involves defining variables, the objective function, and constraints in terms of these variables.

Let's denote:
- $x_1$ as the number of knishes,
- $x_2$ as the amount of protein bars.

The objective function is given as: $1.71 \times x_1^2 + 1.41 \times x_1 \times x_2 + 6.44 \times x_2^2$.

Constraints are:
1. Iron content from knishes and protein bars: $5x_1 + 5x_2 \leq 52$.
2. Minimum iron requirement from squared amounts: $x_1^2 + x_2^2 \geq 46/5$ (since both contain 5 mg of iron).
3. Maximum iron from squared amounts: $x_1^2 + x_2^2 \leq 52/5$.
4. Linear constraint: $6x_1 - 3x_2 \geq 0$.
5. Integer constraint for knishes: $x_1$ must be an integer.

The symbolic representation of the problem is:
```json
{
    'sym_variables': [('x1', 'knishes'), ('x2', 'protein bars')],
    'objective_function': '1.71*x1**2 + 1.41*x1*x2 + 6.44*x2**2',
    'constraints': [
        '5*x1 + 5*x2 <= 52',
        'x1**2 + x2**2 >= 46/5',
        'x1**2 + x2**2 <= 52/5',
        '6*x1 - 3*x2 >= 0'
    ]
}
```

To solve this optimization problem using Gurobi, we'll write the corresponding Python code:

```python
from gurobipy import *

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

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

# Set the objective function
m.setObjective(1.71*x1**2 + 1.41*x1*x2 + 6.44*x2**2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(5*x1 + 5*x2 <= 52, name="iron_content")
m.addConstr(x1**2 + x2**2 >= 46/5, name="min_iron_squared")
m.addConstr(x1**2 + x2**2 <= 52/5, name="max_iron_squared")
m.addConstr(6*x1 - 3*x2 >= 0, name="linear_constraint")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Knishes: {x1.x}")
    print(f"Protein Bars: {x2.x}")
    print(f"Objective Function Value: {m.objVal}")
else:
    print("No optimal solution found")
```