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 in terms of these variables.

Let's denote:
- $x_1$ as the quantity of cantaloupes,
- $x_2$ as the quantity of potatoes.

The objective function described is to minimize: 
\[4x_1^2 + 2x_1x_2 + 9x_2^2 + 9x_1\]

Constraints are:
1. Calcium from cantaloupes and potatoes must be at least 20 milligrams: \[3.42x_1 + 2.7x_2 \geq 20\]
2. The total cost of cantaloupes and potatoes must be at least $28: \[2.08x_1 + 2.45x_2 \geq 28\]
3. The expression \(3x_1 - 10x_2 \geq 0\) must hold.
4. Calcium from cantaloupes and potatoes must not exceed 60 milligrams: \[3.42x_1 + 2.7x_2 \leq 60\]
5. The total cost of cantaloupes and potatoes must not exceed $73: \[2.08x_1 + 2.45x_2 \leq 86\] (Note: This constraint seems to have been misstated in the problem description as "At maximum, you can spend $73 on cantaloupes, and potatoes." but given the upper bound of 'r1' is 86, we'll use that for consistency with provided data.)
6. \(x_1\) must be an integer (non-fractional number of cantaloupes).
7. \(x_2\) can be any non-negative real number.

The symbolic representation in JSON format is:
```json
{
    'sym_variables': [('x1', 'cantaloupes'), ('x2', 'potatoes')],
    'objective_function': '4*x1**2 + 2*x1*x2 + 9*x2**2 + 9*x1',
    'constraints': [
        '3.42*x1 + 2.7*x2 >= 20',
        '2.08*x1 + 2.45*x2 >= 28',
        '3*x1 - 10*x2 >= 0',
        '3.42*x1 + 2.7*x2 <= 60',
        '2.08*x1 + 2.45*x2 <= 86'
    ]
}
```

Now, let's write the Gurobi code to solve this optimization problem:
```python
from gurobipy import *

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

# Add variables
x1 = m.addVar(vtype=GRB.INTEGER, name="cantaloupes")
x2 = m.addVar(vtype=GRB.CONTINUOUS, name="potatoes", lb=0)

# Set the objective function
m.setObjective(4*x1**2 + 2*x1*x2 + 9*x2**2 + 9*x1, GRB.MINIMIZE)

# Add constraints
m.addConstr(3.42*x1 + 2.7*x2 >= 20, name="calcium_min")
m.addConstr(2.08*x1 + 2.45*x2 >= 28, name="cost_min")
m.addConstr(3*x1 - 10*x2 >= 0, name="expression_constraint")
m.addConstr(3.42*x1 + 2.7*x2 <= 60, name="calcium_max")
m.addConstr(2.08*x1 + 2.45*x2 <= 86, name="cost_max")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found.")
    print("Cantaloupes:", x1.x)
    print("Potatoes:", x2.x)
else:
    print("No optimal solution found.")
```