## Symbolic Representation

Let's denote:
- $x_1$ as the acres of peaches
- $x_2$ as the acres of nectarines

The objective is to maximize profit, given that the profit per acre of peaches is $300 and per acre of nectarines is $350. Therefore, the objective function can be represented as:

Maximize: $300x_1 + 350x_2$

The constraints based on the given resources are:
- Bug-spray cost: $40x_1 + 50x_2 \leq 1350$
- Time to spray bug-spray: $50x_1 + 70x_2 \leq 2000$
- Non-negativity and acres of land: $x_1 \geq 0, x_2 \geq 0, x_1 + x_2 \leq 30$

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'acres of peaches'), ('x2', 'acres of nectarines')],
    'objective_function': '300*x1 + 350*x2',
    'constraints': [
        '40*x1 + 50*x2 <= 1350',
        '50*x1 + 70*x2 <= 2000',
        'x1 >= 0',
        'x2 >= 0',
        'x1 + x2 <= 30'
    ]
}
```

## Gurobi Code in Python

```python
import gurobipy as gp

# Create a new model
model = gp.Model("Peaches_and_Nectarines")

# Define variables
x1 = model.addVar(name="peaches", lb=0)  # Acres of peaches
x2 = model.addVar(name="nectarines", lb=0)  # Acres of nectarines

# Objective function: Maximize profit
model.setObjective(300*x1 + 350*x2, gp.GRB.MAXIMIZE)

# Constraints
model.addConstr(40*x1 + 50*x2 <= 1350, name="bug_spray_cost")  # Bug-spray cost constraint
model.addConstr(50*x1 + 70*x2 <= 2000, name="spray_time")  # Time to spray bug-spray constraint
model.addConstr(x1 + x2 <= 30, name="land_availability")  # Acres of land constraint

# Solve the model
model.optimize()

# Print the results
if model.status == gp.GRB.OPTIMAL:
    print(f"Optimal acres of peaches: {x1.varValue}")
    print(f"Optimal acres of nectarines: {x2.varValue}")
    print(f"Maximal profit: {model.objVal}")
else:
    print("The model is infeasible.")
```