To solve the given optimization problem, we first need to convert the natural language description into a symbolic representation. This involves defining variables and formulating the objective function and constraints.

Let's denote:
- \(x_1\) as the kilograms of basmati rice produced.
- \(x_2\) as the kilograms of bananas produced.

The objective is to maximize profit, given that the net profit per kilogram of basmati rice is $15 and per kilogram of bananas is $28. Therefore, the objective function can be written as:
\[ \text{Maximize: } 15x_1 + 28x_2 \]

The constraints are based on the limited resources available:
- Human labor: \(1.5x_1 + 2x_2 \leq 90\)
- Machine work: \(2x_1 + 4x_2 \leq 150\)
- Time under the sun: \(3x_1 + 1.5x_2 \leq 70\)

Additionally, we have non-negativity constraints since the production cannot be negative:
- \(x_1 \geq 0\)
- \(x_2 \geq 0\)

The symbolic representation of the problem in JSON format is:

```json
{
    'sym_variables': [('x1', 'basmati rice'), ('x2', 'bananas')],
    'objective_function': '15*x1 + 28*x2',
    'constraints': [
        '1.5*x1 + 2*x2 <= 90',
        '2*x1 + 4*x2 <= 150',
        '3*x1 + 1.5*x2 <= 70',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

To solve this problem using Gurobi in Python, we can use the following code:

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="basmati_rice")
x2 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="bananas")

# Set objective function
m.setObjective(15*x1 + 28*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(1.5*x1 + 2*x2 <= 90, "human_labor")
m.addConstr(2*x1 + 4*x2 <= 150, "machine_work")
m.addConstr(3*x1 + 1.5*x2 <= 70, "time_under_sun")

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Basmati rice: {x1.x} kg")
    print(f"Bananas: {x2.x} kg")
    print(f"Max Profit: ${m.objVal}")
else:
    print("No optimal solution found")

```