## Problem Description and Symbolic Representation

First, let's define the variables and the problem in a symbolic way:

- $x_1$ = amount invested in the cigarette industry
- $x_2$ = amount invested in the tobacco industry

The objective is to maximize profit. Each dollar invested in the cigarette industry yields a $0.30 profit, and each dollar invested in the tobacco industry yields a $0.45 profit. Therefore, the objective function can be represented as:

Maximize $0.30x_1 + 0.45x_2$

The constraints given are:
1. Total investment not exceeding $5000: $x_1 + x_2 \leq 5000$
2. At least 40% of all the money invested must be in the cigarette industry: $x_1 \geq 0.4(x_1 + x_2)$
3. At least $1000 must be invested in the tobacco industry: $x_2 \geq 1000$
4. Non-negativity constraints: $x_1 \geq 0, x_2 \geq 0$

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'amount invested in the cigarette industry'), ('x2', 'amount invested in the tobacco industry')],
    'objective_function': '0.30*x1 + 0.45*x2',
    'constraints': [
        'x1 + x2 <= 5000',
        'x1 >= 0.4*(x1 + x2)',
        'x2 >= 1000',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Gurobi Code in Python

```python
import gurobipy as gp

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

# Define variables
x1 = model.addVar(name="cigarette_investment", lb=0)  # Investment in cigarette industry
x2 = model.addVar(name="tobacco_investment", lb=0)   # Investment in tobacco industry

# Objective function: Maximize profit
model.setObjective(0.30*x1 + 0.45*x2, gp.GRB.MAXIMIZE)

# Constraints
model.addConstr(x1 + x2 <= 5000, name="total_investment")
model.addConstr(x1 >= 0.4*(x1 + x2), name="cigarette_proportion")
model.addConstr(x2 >= 1000, name="tobacco_min_investment")

# Solve the model
model.optimize()

# Print the results
if model.status == gp.GRB.OPTIMAL:
    print(f"Optimal investment in cigarette industry: ${x1.varValue}")
    print(f"Optimal investment in tobacco industry: ${x2.varValue}")
    print(f"Maximal profit: ${0.30*x1.varValue + 0.45*x2.varValue}")
else:
    print("The model is infeasible.")
```